The DOI matches its Crossref record; 359 of the 363 references it cites were found in a registry or an index, and 1 are not checked yet; 5 of the 164 sources its claims cite were checked for support.
Using machine learning approaches for multi-omics data analysis: A review
Open accessAbstract
With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivering of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.
Verified 2026-10-09 · 1 reference not checked yet
Overview
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Written from the paper’s full text.
TL;DR
This review surveys machine learning approaches for integrating multi-omics data, organizing methods into concatenation-, model-, and transformation-based strategies for supervised and unsupervised learning. It provides background, application examples, challenges, and a recommendation flowchart for interdisciplinary method selection.
Key findings
- Multi-omics integration methods are grouped into three main classes: concatenation-based, model-based, and transformation-based, each with distinct advantages and disadvantages for combining omics data.
- The review reports that most multi-omics studies focus on cancer, especially breast and ovarian cancer, and that integration can improve understanding of disease complexity beyond single-omics analysis.
- It cites examples including an ADNI study using neuroimaging, biochemical, and genetic biomarkers to discriminate early Alzheimer’s patients from healthy volunteers with 98% accuracy, and notes class imbalance examples such as primary hypertension at 95% prevalence versus endocrine hypertension at 5%.
- A recommendation flowchart is proposed to help interdisciplinary teams choose an appropriate integration method, but the paper states that no “one-size-fits-all” approach is feasible.
Methods
- Narrative review of supervised and unsupervised ML strategies for multi-omics integration, explicitly excluding reinforcement, hybrid, multi-view, and self-supervised learning because they have not gained enough momentum in multi-omics analysis.
- Summarizes integration methods, published multi-omics studies, ML terminology, algorithms, and advantages/disadvantages in tables, and presents a recommendation flowchart.
- No primary dataset or experimental analysis is reported; the paper synthesizes existing literature and examples.
Limitations
- The authors state that existing literature does not provide many direct comparisons between methods using the same publicly available datasets, and that empirical investigation guided by ML practitioners is recommended.
- The review’s scope excludes several emerging ML specializations, including reinforcement, hybrid, multi-view, and self-supervised learning.
- Evident from the text: this is a narrative review without a systematic search protocol or quantitative synthesis, and the provided version is a journal pre-proof with production artifacts.
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Claims the paper makes
107 Not checked yet · 29 No basis given · 7 Own interpretation · 2 Own result · 1 Supported · 1 Supported so far · 1 Partly supported · accuracy not yet measured
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In the paper
“Health-based big data including linked information for patients, such as their clinical data (for example gender, age, pathological and physiological history) and omics data (such as genetics, proteomics and metabolomics) has now become more widely available (Canuel et al., 2015;Singhal et al., 2016).”
§ Using machine learning approaches for multi-omics data analysis > 1 INTRODUCTION
What the sources say
- Supportedcites Canuel et al., 2015 source
The Introduction describes a growing amount of omics data and the use of clinical data warehouses, while the Conclusion refers to an “explosion of data available” for work combining clinical and omics data. The paper therefore supports the broad claim of increasing availability, without implying that patient-level clinical and omics records are already routinely linked. (LLM, conf=0.86)
- Could not verifycites Singhal et al., 2016 sourceExperimental — accuracy not yet measured
The Introduction discusses using patients’ genetic mutations in precision medicine and curating genotype–phenotype information from biomedical literature. The Methods describe extracting information from PubMed abstracts. Neither section says that linked patient clinical and omics data have become more widely available (searched for availability of clinical, genomic, proteomic and metabolomic data). (LLM, conf=0.75)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“The commonly used ML algorithms have different attributes (Table 4) and therefore it is crucial to choose an appropriate algorithm for the multi-omics analysis.”
§ Using machine learning approaches for multi-omics data analysis > 3.5 What algorithm works best for what conditions?
What the sources say
- Supportedcites Amancio et al., 2014 source
The Conclusions (page not provided) report that different classifiers show distinct performance patterns as the number of features changes. This supports the attributed point that commonly used machine-learning algorithms differ in their characteristics; the multi-omics recommendation is the citing authors’ application of that point. (LLM, conf=0.90)
- Supportedcites Barredo Arrieta et al., 2020 source
Section 3.2 (p. 15) contrasts decision trees’ transparency and generalization properties with those of other models. Section 4.2 (p. 21) likewise distinguishes interpretable models from models requiring additional explanation. These passages support the attributed point that commonly used ML algorithms have different attributes; the separate recommendation about multi-omics analysis is not part of the quoted claim. (LLM, conf=0.92)
- Not checked yetcites Sakr et al., 2017
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Joint NMF (non-negative matrix factorisation) (Zhang et al., 2012) was proposed to integrate multi-omics data with non-negative values.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Supportedcites Zhang et al., 2012 source
The Materials and Methods section, under “The joint NMF framework for integrative analysis,” describes jointly factorizing three types of genomic data from the same samples. “The NMF problem” specifies non-negative input data and factors. The supplied passages do not give page numbers. (LLM, conf=0.91)
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is evident from Table 8 that most of the multi-omics studies focus on different forms of cancer.”
§ Using machine learning approaches for multi-omics data analysis > 5 APPLICATION OF INTEGRATIVE METHODS IN MULTI-OMICS STUDIES
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Many intra-omics studies have successfully explored the integration of gene expression and DNA methylation.”
§ Using machine learning approaches for multi-omics data analysis > 5 APPLICATION OF INTEGRATIVE METHODS IN MULTI-OMICS STUDIES
plaincorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Currently, many of the multi-omic reviews are focussed on individual sub-topics.For example, designing studies (Haas et al., 2017;Hasin et al., 2017), setting up workflows (Kohl et al., 2014), choosing software tools (Misra et al., 2019) and evaluating overfitted performance (McCabe et al., 2020).”
§ Using machine learning approaches for multi-omics data analysis > 1 INTRODUCTION
What the sources say
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In the paper
“There has been unprecedented growth in the development of precision medicine supported by ML (machine learning) approaches (Delavan et al., 2017;Peterson et al., 2013;Zou et al., 2017) and data mining tools (Chawla and Davis, 2013;Cheng et al., 2015;Margolies et al., 2016).”
§ Using machine learning approaches for multi-omics data analysis > 1 INTRODUCTION
What the sources say
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In the paper
“High-throughput generated omics data (Lightbody et al., 2019) has played a pivotal role in developing precision medicine biomarkers for diseases such as Alzheimer's (Hampel et al., 2017(Hampel et al., , 2016;;Kovacs, 2016), diabetes (Capobianco, 2017;McCarthy, 2017;Mutie et al., 2017), cancer (Borad and LoRusso, 2017;Senft et al., 2017), hypertension (Barnes et al., 2016;Dominiczak et al., 2017),”
§ Using machine learning approaches for multi-omics data analysis > 2.1 Multi-omics
What the sources say
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In the paper
“It has pushed the performance of classifiers beyond that of traditional ML algorithms, especially in scenarios involving large-scale datasets with high dimensionality.”
§ Using machine learning approaches for multi-omics data analysis > 2.2 Machine learning
What the sources say
- Not checked yetcites LeCun et al., 2015 source
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In the paper
“Recently, such data has been used for precision (also called personalised or stratified) medicine to provide customised healthcare, i.e. providing a bespoke treatment for individuals (Gibson et al., 2015;Kalaitzopoulos, 2016;Malod-Dognin et al., 2017).”
§ Using machine learning approaches for multi-omics data analysis > 1 INTRODUCTION
What the sources say
- Not checked yetcites Gibson et al., 2015
- Not checked yetcites Kalaitzopoulos, 2016
- Not checked yetcites Malod-Dognin et al., 2017
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In the paper
“Figure 1 shows the number of publications indexed on the Web of Science website (Clarivate Analytics, 2020) with different key topics.This information was collected from the Web of Science by entering a different keyword and searching across all databases 2 .Although the use of ML in medical science can be dated back to 1970s (Davenport and Kalakota, 2019), more rapid growth is evident in the past”
§ Using machine learning approaches for multi-omics data analysis > Bias-variance trade-off
What the sources say
- Not checked yetcites Clarivate Analytics, 2020
- Not checked yetcites Davenport and Kalakota, 2019 source
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In the paper
“Moreover, publications based on ‗multi-omics integration' and ‗multi-omics and machine learning' have started to emerge in the last 5 years and have gained popularity in precision and computational medicine domain.”
§ Using machine learning approaches for multi-omics data analysis > Bias-variance trade-off
What the sources say
- Not checked yetcites Clarivate Analytics, 2020
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Today a plethora of multi-omic integration methods are available for both supervised and unsupervised learning as evident in the current review.”
§ Using machine learning approaches for multi-omics data analysis > 6 RECOMMENDATIONS
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Also, unfortunately, the existing literature does not provide many direct comparisons between methods using the same publicly available datasets.”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Deep learning has emerged in recent years as the leading class of ML algorithms.”
§ Using machine learning approaches for multi-omics data analysis > 2.2 Machine learning
What the sources say
- Not checked yetcites LeCun et al., 2015 source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Although various ML specialisations such as reinforcement (Coronato et al., 2020), hybrid (Zhou et al., 2019), multi-view (Zhao et al., 2017) and selfsupervised learning (Chen et al., 2019) are now emerging in generic health-care applications, they have not yet gained enough momentum in multi-omics analysis, hence they remain beyond the scope of this review.”
§ Using machine learning approaches for multi-omics data analysis > 1 INTRODUCTION
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Although ML analysis of multi-omics is still in its embryonic stage, it has already been explored for a wide range of applications, as reported in recent reviews on brain diseases (Garali et al., 2018;Young et al., 2013), diabetes (Kavakiotis et al., 2017), cancers (Borad and LoRusso, 2017;Chaudhary et al., 2017;Wong et al., 2016) cardiovascular disease (Weng et al., 2017), medical imaging (Ericks”
§ Using machine learning approaches for multi-omics data analysis > 1 INTRODUCTION
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“In recent years, with the increasing availability of affordable computing power and high-throughput omics data and the success of artificial intelligence technology in various fields, the use of ML has become popular in health sciences (Lee and Yoon, 2017;Clifton et al., 2015;Hung, 2019;Barnett-Itzhaki et al., 2020;Kirchebner et al., 2020).”
§ Using machine learning approaches for multi-omics data analysis > 2.2 Machine learning
What the sources say
- Not checked yetcites Lee and Yoon, 2017
- Not checked yetcites Clifton et al., 2015
- Not checked yetcites Hung, 2019 source
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In the paper
“Recently, these omics have also been integrated for COVID-19 studies (Barh et al., 2020;Overmyer et al., 2020;Zhou et al., 2020).”
§ Using machine learning approaches for multi-omics data analysis > 2.1 Multi-omics
What the sources say
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In the paper
“Overall, these omics provide a complete picture of cell biology and related cellular function (Cox, 2009).”
§ Using machine learning approaches for multi-omics data analysis > 2.1 Multi-omics
What the sources say
- Not checked yetcites Cox, 2009 source
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In the paper
“On the other hand, it is very computationally intensive, requiring highthroughput or high-performance hardware, and lacks explainability (transparency) in feature selection (black-box approach), in the sense that it is difficult to extract from the network the features that the network has found as mainly responsible for the task, e.g.classification (LeCun et al., 2015).”
§ Using machine learning approaches for multi-omics data analysis > 2.2 Machine learning
What the sources say
- Not checked yetcites LeCun et al., 2015 source
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In the paper
“For example, primary hypertension is the most common form of hypertension with 95% prevalence while endocrine hypertension occurs in only 5% (Rimoldi et al., 2014).”
§ Using machine learning approaches for multi-omics data analysis > 3.2 Class imbalance and overfitting
What the sources say
- Not checked yetcites Rimoldi et al., 2014 source
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In the paper
“Similarly, techniques such as regularisation, bagging, hyperparameter tuning and cross-validation can used to balance bias-variance trade-off (Lee, 2010).”
§ Using machine learning approaches for multi-omics data analysis > 3.2 Class imbalance and overfitting
What the sources say
- Not checked yetcites Lee, 2010
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In the paper
“However, there are few other challenges related to multi-omics studies which are not ML related such as study design (Haas et al., 2017), multi-site sample collection & management (Pinu et al., 2019), multi-site data sharing and governance (Saulnier et al., 2019), visualisation (Mougin et al., 2018), ethical standards (Lévesque et al., 2018), and finally making the research reproducible (Conesa”
§ Using machine learning approaches for multi-omics data analysis > 3.6 Translating ML: bench to bedside
What the sources say
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since each multi-omics dataset is unique, using a similar workflow could allow the selection of the best-suited algorithm.”
§ Using machine learning approaches for multi-omics data analysis > 3.5 What algorithm works best for what conditions?
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In the paper
“Although deep learning is widely popular in other related domains (such as medical imaging (Erickson et al., 2017) and clinical natural language processing (Wu et al., 2020)), the interest has been more limited for multi-omics analysis (X.Tan et al., 2020).”
§ Using machine learning approaches for multi-omics data analysis > Bias-variance trade-off
What the sources say
- Not checked yetcites Erickson et al., 2017
- Not checked yetcites Wu et al., 2020
- Not checked yetcites X.Tan et al., 2020
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In the paper
“Most of these reviews have strived to introduce different categorical terminologies (for example: ‗'early', ‗late' and ‗intermediate' in (Gligorijević and Pržulj, 2015) or ‗bottom-up' and ‗top-down' in (Yu and Zeng, 2018)) which enable them to group the integration methods based on different factors/parameters.”
§ Using machine learning approaches for multi-omics data analysis > 4 DATA INTEGRATION METHODS FOR MULTI-OMICS
What the sources say
- Not checked yetcites Gligorijević and Pržulj, 2015
- Not checked yetcites Yu and Zeng, 2018
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In the paper
“In recent years, various new data integration methods have been introduced from the modern developments in mathematical, statistical and computational sciences.”
§ Using machine learning approaches for multi-omics data analysis > 4 DATA INTEGRATION METHODS FOR MULTI-OMICS
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In the paper
“In recent years, ML models can be deployed on dedicated graphics processing units (Schmidhuber, 2015) and cloud computing platforms (Armbrust et al., 2010) such as Amazon EC2 (-Amazon EC2,‖ n.d.), Microsoft Azure (-Cloud Computing Services | Microsoft Azure,‖ n.d.) and Google Cloud Platform (-Cloud Computing Services,‖ n.d.).”
§ Using machine learning approaches for multi-omics data analysis > 3.4 Computation and storage cost
What the sources say
- Not checked yetcites Schmidhuber, 2015
- Not checked yetcites Armbrust et al., 2010 source
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All the above challenges directly impact the use of ML for multi-omics analysis.”
§ Using machine learning approaches for multi-omics data analysis > 3.6 Translating ML: bench to bedside
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, transcriptomics and proteomics use different normalisation and scaling techniques before omics analysis.This leads to different dynamic ranges and data distribution.”
§ Using machine learning approaches for multi-omics data analysis > 3 CHALLENGES IN MULTI-OMICS ANALYSIS USING MACHINE LEARNING
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In the paper
“Recently, various artificial intelligence-driven automated ML platforms and tools (Feurer et al., 2015;Olson et al., 2018;Waring et al., 2020) have also emerged which can be utilised to exhaustively search for best ML model and corresponding parameter tuning, however, they are computationally expensive.”
§ Using machine learning approaches for multi-omics data analysis > 3.5 What algorithm works best for what conditions?
What the sources say
- Not checked yetcites Feurer et al., 2015
- Not checked yetcites Olson et al., 2018
- Not checked yetcites Waring et al., 2020 source
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In the paper
“Various ML based multi-omics publications have emerged in the past 5 years (see Figure 1) and some use performance metrics such as decision curve (Vickers and Elkin, 2006) and calibration (Dankers et al., 2019) analytics to evaluate their diagnostic utility.”
§ Using machine learning approaches for multi-omics data analysis > 3.6 Translating ML: bench to bedside
What the sources say
- Not checked yetcites Vickers and Elkin, 2006
- Not checked yetcites Dankers et al., 2019
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In the paper
“In the literature, many reviews cover the key strengths and weaknesses of different ML algorithms using single omics (Amancio et al., 2014;López Pineda et al., 2015;Sakr et al., 2017;Uddin et al., 2019) and multi-omics (Ma et al., 2016;Francescatto et al., 2018;J. Xu et al., 2019;Sathyanarayanan et al., 2020) datasets.”
§ Using machine learning approaches for multi-omics data analysis > 3.5 What algorithm works best for what conditions?
What the sources say
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Joint NMF is computationally slow and needs large memory allocation.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Similarly, Shen (Shen et al., 2009) proposed iCluster framework which used principles similar to NMF but allows integration of datasets having negative values.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Shen et al., 2009 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Fridley (Fridley et al., 2012) has studied the genomic effects due to the gemcitabine drug using high-throughput data from mRNA expression and SNPs.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Fridley et al., 2012 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The iCluster+ framework can discover patterns and combine a range of omics having binary, categorical and continuous values and was demonstrated by combining genomic data from the colorectal cancer datasets.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Mo et al., 2013
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“They integrated these two datasets into one large input matrix and developed a Bayesian pathway analysis which uses a stochastic search variable selection.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Fridley et al., 2012 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Commonly, concatenation-based integration does not require any preprocessing and hence does not have a Stage 2.”
§ Using machine learning approaches for multi-omics data analysis > 4.1 Concatenation-based integration methods
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“joint factor analysis to evaluate the feature space and converted it into shared and datatype-specific components.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Ray et al., 2014 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“In scenarios where the number of features in the joint matrix are higher, different feature selection methods described in Section 3 can be employed during concatenation (Sorzano et al., 2014).”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
What the sources say
- Not checked yetcites Sorzano et al., 2014
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Similarly, multivariate LASSO models (Zou, 2006;Nicolai and Bühlmann Peter, 2010;Mankoo et al., 2011) have been investigated.”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“It was used to investigate gene expression and miRNA data on brain tumour samples.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Lock et al., 2013
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Their proposed that the Bayesian integrative model offers better performance in detecting the genomic effects in comparison to using conventional single-omics analysis.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Fridley et al., 2012 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“iClusterBayes includes a binary indicator prior for selection of variable and generalises for binary data and count data.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Mo et al., 2018
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“It overcomes the limitations of iCluster+, in terms of statistical inference and computational speed.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Mo et al., 2018
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Similarly, Zhu (Zhu et al., 2012) has also explored BN (Bayesian network) to understand cell regulation in yeast using metabolomics and transcriptomics data.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Zhu et al., 2012 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These methods use all the concatenated features equally and can select the most discriminating features for a given phenotype.”
§ Using machine learning approaches for multi-omics data analysis > 4.1 Concatenation-based integration methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Also, LRAcluster (Wu et al., 2015) was developed to integrate high-dimensional multi-omics data and find low-dimensional manifold to identify molecular subtypes of cancer.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Wu et al., 2015
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“MoCluster was validated by integrating proteomic and transcriptomic data and shows a noticeably higher clustering accuracy and lower computation cost in comparison to both Cluster and iCluster+.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Meng et al., 2016a
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Another adaptation of NMF was evaluated as JIVE (Joint and Individual Variation Explained) which captures joint variation across integrating data types and structural variation of each data type along with the residual noise (Lock et al., 2013).”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Lock et al., 2013
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This approach allows the analysis of the improvement in the predictive power for individual models and also finds the best discriminating features.”
§ Using machine learning approaches for multi-omics data analysis > 4.2 Model-based integration methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Recently, iClusterBayes was introduced by Mo (Mo et al., 2018), which is a fully Bayesian latent variable model.”
§ Using machine learning approaches for multi-omics data analysis > 4.1.2 Unsupervised learning concatenation-based methods
What the sources say
- Not checked yetcites Mo et al., 2018
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Recently, MOSAE (Multi-omics Supervised Autoencoder) (K.Tan et al., 2020) was developed for pan-cancer analysis and compared with conventional ML methods such as SVM, DT, naïve Bayes, KNN, RF and AdaBoost.”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
What the sources say
- Not checked yetcites K.Tan et al., 2020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, it is evident from the literature that kernel-based algorithms have superior performance to graph-based approaches, but they usually need more time for the training phase.”
§ Using machine learning approaches for multi-omics data analysis > 4.3.1 Supervised learning transformation-based methods
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Ideally, requires all omics data for all patients.”
§ Using machine learning approaches for multi-omics data analysis > Concatenation-Based
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“It uses grammatical evolution neural networks along with Biofilter (Bush et al., 2009) and Random Jungle (Schwarz et al., 2010) to investigate different categorical and quantitative variables and develop prediction models.”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
What the sources say
- Not checked yetcites Bush et al., 2009
- Not checked yetcites Schwarz et al., 2010 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Does not consider the unique distribution of each omics.”
§ Using machine learning approaches for multi-omics data analysis > Concatenation-Based
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Lemon-Tree (Bonnet et al., 2015) implemented ensemble methods for reconstructing module networks which used somatic copy number alterations and gene expression in brain tumour samples.”
§ Using machine learning approaches for multi-omics data analysis > 4.2.2 Unsupervised learning model-based methods
What the sources say
- Not checked yetcites Bonnet et al., 2015 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Meta-SVM (Meta-analytic SVM) is proposed by Kim (Kim et al., 2017), which integrates multiple omics data and able to detect consensus genes associated with diseases across studies such as breast cancer and idiopathic pulmonary fibrosis.”
§ Using machine learning approaches for multi-omics data analysis > 4.3.2 Unsupervised learning transformation-based methods
What the sources say
- Not checked yetcites Kim et al., 2017 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Similarly, Denoising autoencoder has been incorporated along with L1-penalized logistic regression for identifying ovarian cancer subtypes (Guo et al., 2020).”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
What the sources say
- Not checked yetcites Guo et al., 2020 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Recently, NEMO (NEighborhood based Multi-Omics clustering) (Rappoport and Shamir, 2019) is introduced which uses an inter-patient similarity matrix-based distance metric for evaluating the input omic datasets individually.”
§ Using machine learning approaches for multi-omics data analysis > 4.3.2 Unsupervised learning transformation-based methods
What the sources say
- Not checked yetcites Rappoport and Shamir, 2019 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Need proper normalisation before concatenation.”
§ Using machine learning approaches for multi-omics data analysis > Concatenation-Based
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Does not increase dimensional complexity.”
§ Using machine learning approaches for multi-omics data analysis > Model-Based
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Lately, rMKL-LPP (regularised multiple kernel learning for Locality Preserving Projections) (Speicher and Pfeifer, 2015) was implemented for clustering analysis.”
§ Using machine learning approaches for multi-omics data analysis > 4.3.2 Unsupervised learning transformation-based methods
What the sources say
- Not checked yetcites Speicher and Pfeifer, 2015
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Most of them are kernel and graph-based algorithms (Yan et al., 2017).”
§ Using machine learning approaches for multi-omics data analysis > 4.3.1 Supervised learning transformation-based methods
What the sources say
- Not checked yetcites Yan et al., 2017 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Not effective if omics data is extremely heterogeneous.”
§ Using machine learning approaches for multi-omics data analysis > Model-Based
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“In the past years, ATHENA (Analysis Tool for Heritable and Environmental Network Associations) was developed for analysing multi-omics data (Chung and Kang, 2019;Holzinger et al., 2014).”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
What the sources say
- Not checked yetcites Chung and Kang, 2019
- Not checked yetcites Holzinger et al., 2014 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Graph representation easy to understand and computationally less intensive.”
§ Using machine learning approaches for multi-omics data analysis > Transformation-Based
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Facilitates the understanding of interactions amongst different omics.”
§ Using machine learning approaches for multi-omics data analysis > Model-Based
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Kernel methods provide superior performance.”
§ Using machine learning approaches for multi-omics data analysis > Transformation-Based
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. The quote only partly states this claim.
In the paper
“It uses a two-step process and limits the integration to only two datatypes.”
§ Using machine learning approaches for multi-omics data analysis > 4.2.2 Unsupervised learning model-based methods
What the sources say
- Not checked yetcites Yuan et al., 2011 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Omics data can be from a different set of patients with a similar phenotype.”
§ Using machine learning approaches for multi-omics data analysis > Model-Based
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Transformation can be sometimes challenging.”
§ Using machine learning approaches for multi-omics data analysis > Transformation-Based
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Later, these representations are integrated to predict cancer subtype classification.”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
What the sources say
- Not checked yetcites J.Xu et al., 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Memory and computation-intensive when the concatenated matrix is large.”
§ Using machine learning approaches for multi-omics data analysis > Concatenation-Based
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“MOLI (multi-omics late integration) (Sharifi-Noghabi et al., 2019) method used type-specific encoding sub-networks to learn features from somatic mutation, CNA and gene expression data independently and then later concatenated them for predicting the response to a given drug.”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
What the sources say
- Not checked yetcites Sharifi-Noghabi et al., 2019 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, graph-based approaches can disclose the relations between samples while taking less computation time.”
§ Using machine learning approaches for multi-omics data analysis > 4.3.1 Supervised learning transformation-based methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Multi-omics data for the same patient can be used for their disease subgroup analysis.”
§ Using machine learning approaches for multi-omics data analysis > Transformation-Based
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“These omics matrices are then combined into one matrix and then analysed using spectral-based clustering.”
§ Using machine learning approaches for multi-omics data analysis > 4.3.2 Unsupervised learning transformation-based methods
What the sources say
- Not checked yetcites Rappoport and Shamir, 2019 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In particular, the presence of many multi-omics studies related to breast (Chen et al., 2017;Lee et al., 2017;List et al., 2014;Ma et al., 2016;Nam et al., 2009) and ovarian (Anděl et al., 2015;Mankoo et al., 2011;Paik et al., 2017;Zhang et al., 2014) cancer highlights the research thrust by the scientific community in these domains.”
§ Using machine learning approaches for multi-omics data analysis > 5 APPLICATION OF INTEGRATIVE METHODS IN MULTI-OMICS STUDIES
What the sources say
- Not checked yetcites Chen et al., 2017 source
- Not checked yetcites Anděl et al., 2015
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Furthermore, SNF (Similarity Network Fusion) (Wang et al., 2014) constructs networks of samples for respective data type and then effectively fuse them into a joint network which denotes the complete range of original data.”
§ Using machine learning approaches for multi-omics data analysis > 4.2.2 Unsupervised learning model-based methods
What the sources say
- Not checked yetcites Wang et al., 2014 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Weak signals could be lost.”
§ Using machine learning approaches for multi-omics data analysis > Model-Based
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Kernel methods are computationally more intensive than graph methods.”
§ Using machine learning approaches for multi-omics data analysis > Transformation-Based
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Could lead to an overfitted solution”
§ Using machine learning approaches for multi-omics data analysis > Model-Based
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the different recent multi-omics studies highlight the superiority of integration methods in understanding the complexity of different diseases and uncovering the underlying abnormalities from the vastly generated multi-omics data, which is not always possible with individual omics analysis.”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Metabolomics and proteomics have been integrated using RF for analysis of prostate cancer (Fan et al., 2011) and thyroid functioning (Pietzner et al., 2017).”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Similarly, metabolomics is integrated with mRNA for studying ulcerative colitis (Bjerrum et al., 2014) and cancer survival (Kim et al., 2014).”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
What the sources say
- Not checked yetcites Bjerrum et al., 2014
- Not checked yetcites Kim et al., 2014 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“LASSO methods have been used for this particular integration by Taskesen (Taskesen et al., 2015) and Lee (Lee et al., 2017) for acute myeloid leukaemia and breast cancer respectively.”
§ Using machine learning approaches for multi-omics data analysis > 5 APPLICATION OF INTEGRATIVE METHODS IN MULTI-OMICS STUDIES
What the sources say
- Not checked yetcites Taskesen et al., 2015 source
- Not checked yetcites Lee et al., 2017
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Clearly, a ‗one-size-fits-all' approach is not feasible.”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“Multi-omics studies have also been successfully conducted in plants (potato (Acharjee et al., 2016(Acharjee et al., , 2011))) and animals (such as canine heart disease (Li et al., 2015)).”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
What the sources say
- Not checked yetcites Acharjee et al., 2016 source
- Not checked yetcites Li et al., 2015
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Similarly, mRNA -miRNA integration was investigated using Neural Fuzzy Network for colorectal cancer (Vineetha et al., 2013), SVM for pancreatic cancer (Kwon et al., 2015), and RF for cardiac tissue ageing (Dimitrakopoulos et al., 2014) and ovarian cancer (Anděl et al., 2015) respectively.”
§ Using machine learning approaches for multi-omics data analysis > J o u r n a l P r e -p r o o f
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“With increasingly sophisticated -omics technologies, the dimensionality of data increases rapidly, thereby surpassing the number of cases, which remains similar over time. This phenomenon is described as the “curse of dimensionality”44.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [44]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Using machine learning approaches for multi-omics data analysis > 4.1 Concatenation-based integration methods
“Commonly, concatenation-based integration does not require any preprocessing and hence does not have a Stage 2.”
§ Using machine learning approaches for multi-omics data analysis > Concatenation-Based
“ Need proper normalisation before concatenation.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Using machine learning approaches for multi-omics data analysis > 4.2 Model-based integration methods
“The final multi-dimensional joint model in Stage 4 can be built using an ML algorithm (such as neural networks) which uses the most relevant variables from each omics models (from Stage 3).”
§ Using machine learning approaches for multi-omics data analysis > 4.2 Model-based integration methods
“In Stage 2, individual models are developed for each of the omics which are later integrated into a joint model in Stage 3.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Using machine learning approaches for multi-omics data analysis > 5 APPLICATION OF INTEGRATIVE METHODS IN MULTI-OMICS STUDIES
“Similarly, transcriptomics is further classed into lncRNAs (long non-coding RNAs) and microRNAs (mRNA and miRNA).”
§ Using machine learning approaches for multi-omics data analysis > 2.1 Multi-omics
“mRNA (messenger ribo-nucleic acid)”
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
DNA sequencing with chain-terminating inhibitors
Sanger, F., Nicklen, S., & Coulson, A. R. (1977) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.74.12.5463 · Cited by 68,572
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
(1992) · Cited by 23,431
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Neural networks for pattern recognition
Bishop, C. (1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,537
Jolliffe, I. T. (2005) · Wiley eBooks · doi:10.1002/0470013192.bsa501 · Cited by 14,522
The Pfam Protein Families Database
Bateman, A. G. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.276 · Cited by 14,075
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Pattern Recognition and Machine Learning
(2006) · doi:10.1007/978-0-387-45528-0 · Cited by 9,824
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al. (2010) · Communications of the ACM · doi:10.1145/1721654.1721672 · Cited by 9,464
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 8,354
(2006) · Springer eBooks · Cited by 8,123
UniProt: a worldwide hub of protein knowledge
The UniProt Consortium (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1049 · Cited by 7,698
The adaptive lasso and its oracle properties
Zou, H. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000735 · Cited by 7,470
Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001) · Advanced textbooks in control and signal processing · doi:10.1007/978-1-4471-0347-9_4 · Cited by 6,684
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
A working guide to boosted regression trees
Elith, J., Leathwick, J. R., & Hastie, T. (2008) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2008.01390.x · Cited by 6,484
Decision curve analysis: a novel method for evaluating prediction models
Vickers, A. J., & Elkin, E. B. (2006) · Medical Decision Making · doi:10.1177/0272989x06295361 · Cited by 6,341
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
METABRIC Group, Curtis, C., Shah, S. P., Chin, S.-F., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., et al. (2012) · Nature · doi:10.1038/nature10983 · Cited by 6,097
Radiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
Altman, N. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475879 · Cited by 5,657
Lodish, H. F. (1986) · Cited by 5,650
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
He, H., Bai, Y., Garcia, E. A., & Shutao Li, A. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4633969 · Cited by 5,271
miRBase: from microRNA sequences to function
Kozomara, A., Birgaoanu, M., & Griffiths-Jones, S. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1141 · Cited by 4,843
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051
The potential for artificial intelligence in healthcare
Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800
Abe, S. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0285-4 · Cited by 3,758
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
Meyer, F., Paarmann, D., D'Souza, M., Olson, R., Glass, E., Kubal, M., Paczian, T., Rodriguez, A., Stevens, R. L., Wilke, A., et al. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-386 · Cited by 3,565
Multiple imputation by chained equations: what is it and how does it work?
Azur, M. J., Stuart, E. A., Frangakis, C., & Leaf, P. J. (2011) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.329 · Cited by 3,542
HMDB 4.0: the human metabolome database for 2018
Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., Sajed, T., Johnson, D., Li, C., Karu, N., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1089 · Cited by 3,438
Martinez, A. M., & Kak, A. C. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908974 · Cited by 3,249
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Domingos, P., & Pazzani, M. J. (1997) · Machine Learning · doi:10.1023/a:1007413511361 · Cited by 3,175
Floating search methods in feature selection
Pudil, P., Novovičová, J., & Kittler, J. (1994) · Pattern Recognition Letters · doi:10.1016/0167-8655(94)90127-9 · Cited by 3,130
Multi-omics approaches to disease
Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953
The Pfam protein families database
Finn, R. D., Mistry, J., Tate, J., Coggill, P., Heger, A., Pollington, J. E., Gavin, O. L., Gunasekaran, P., Ceric, G., Forslund, K., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp985 · Cited by 2,694
Meinshausen, N., & Bühlmann, P. (2010) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2010.00740.x · Cited by 2,463
Towards a knowledge-based Human Protein Atlas
Uhlen, M., Oksvold, P., Fagerberg, L., Lundberg, E., Jonasson, K., Forsberg, M., Zwahlen, M., Kampf, C., Wester, K., Hober, S., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt1210-1248 · Cited by 2,440
Strimbu, K., & Tavel, J. A. (2010) · Current Opinion in HIV and AIDS · doi:10.1097/coh.0b013e32833ed177 · Cited by 2,062
XAI—Explainable artificial intelligence
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G.-Z. (2019) · Science Robotics · doi:10.1126/scirobotics.aay7120 · Cited by 2,053
Similarity network fusion for aggregating data types on a genomic scale
Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., & Goldenberg, A. (2014) · Nature Methods · doi:10.1038/nmeth.2810 · Cited by 2,036
Handelsman, J., Rondon, M. R., Brady, S. F., Clardy, J., & Goodman, R. M. (1998) · Chemistry & Biology · doi:10.1016/s1074-5521(98)90108-9 · Cited by 2,007
A Comprehensive Survey of Clustering Algorithms
Xu, D., & Tian, Y. (2015) · Annals of Data Science · doi:10.1007/s40745-015-0040-1 · Cited by 1,939
Neural networks for pattern recognition
Marshall, J. A. (1995) · Neural Networks · doi:10.1016/0893-6080(95)90002-0 · Cited by 1,888
Single-cell RNA sequencing technologies and bioinformatics pipelines
Hwang, B., Lee, J. H., & Bang, D. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0071-8 · Cited by 1,866
Awad, M., & Khanna, R. (2015) · Apress eBooks · doi:10.1007/978-1-4302-5990-9_4 · Cited by 1,830
Comparing different supervised machine learning algorithms for disease prediction
Uddin, S., Khan, A. M., Hossain, M. E., & Moni, M. A. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-1004-8 · Cited by 1,759
Machine learning for medical imaging
Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017) · RadioGraphics · doi:10.1148/rg.2017160130 · Cited by 1,672
Awad, M., & Khanna, R. (2015) · Apress eBooks · doi:10.1007/978-1-4302-5990-9 · Cited by 1,625
Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets
Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178124 · Cited by 1,510
Machine learning and data mining methods in diabetes research
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445
High-Throughput Sequencing Technologies
Reuter, J., Spacek, D. V., & Snyder, M. P. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.05.004 · Cited by 1,404
LMSD: LIPID MAPS structure database
Sud, M., Fahy, E., Cotter, D., Brown, A., Dennis, E. A., Glass, C. K., Merrill, A. H., Murphy, R. C., Raetz, C. R. H., Russell, D. W., et al. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl838 · Cited by 1,388
Badue, C. S., Guidolini, R., Carneiro, R. V., Azevedo, P., Cardoso, V. B., Forechi, A., Jesus, L. F. R., Berriel, R. F., Paixão, T. M., Mutz, F. W., et al. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113816 · Cited by 1,347
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017) · PLOS ONE · doi:10.1371/journal.pone.0174944 · Cited by 1,346
Lowe, R. G. T., Shirley, N. J., Bleackley, M. R., Dolan, S., & Shafee, T. M. A. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005457 · Cited by 1,307
(2015) · neural information processing systems · Cited by 1,305
Benjamens, S., Dhunnoo, P., & Meskó, B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00324-0 · Cited by 1,215
Deep Learning vs. Traditional Computer Vision
O’Mahony, N., Campbell, S., Carvalho, A. L. D., Harapanahalli, S., Velasco-Hernandez, G., Krpálková, L., Riordan, D., & Walsh, J. L. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-17795-9_10 · Cited by 1,173
Protein Data Bank: the single global archive for 3D macromolecular structure data
wwPDB consortium, Burley, S. K., Berman, H. M., Bhikadiya, C., Bi, C., Chen, L., Costanzo, L. D., Christie, C., Duarte, J. M., Dutta, S., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky949 · Cited by 1,163
(1987) · Comparative Biochemistry and Physiology Part A Physiology · doi:10.1016/0300-9629(87)90455-5 · Cited by 1,130
Deep learning-based multi-omics integration robustly predicts survival in liver cancer
Chaudhary, K., Poirion, O. B., Lu, L., & Garmire, L. X. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-0853 · Cited by 1,091
Methods of integrating data to uncover genotype-phenotype interactions
Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A., & Kim, D. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3868 · Cited by 1,085
Proteomics: Technologies and Their Applications
Aslam, B., Basit, M., Nisar, M. A., Khurshid, M., & Rasool, M. H. (2016) · Journal of Chromatographic Science · doi:10.1093/chromsci/bmw167 · Cited by 1,070
Multi-view learning overview: Recent progress and new challenges
Zhao, J., Xie, X., Xu, X., & Sun, S. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.007 · Cited by 1,002
Comparison of RNA-Seq and Microarray in Transcriptome Profiling of Activated T Cells
Zhao, S., Fung‐Leung, W., Bittner, A., Ngo, K., & Liu, X. (2014) · PLoS ONE · doi:10.1371/journal.pone.0078644 · Cited by 1,000
Supervised Machine Learning: A Brief Primer
Jiang, T., Gradus, J. L., & Rosellini, A. J. (2020) · Behavior Therapy · doi:10.1016/j.beth.2020.05.002 · Cited by 953
Metabolomics by Gas Chromatography–Mass Spectrometry: Combined Targeted and Untargeted Profiling
Fiehn, O. (2016) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb3004s114 · Cited by 917
An Introduction to Machine Learning
Badillo, S., Bánfai, B., Birzele, F., Davydov, I. I., Hutchinson, L. G., Kam‐Thong, T., Siebourg‐Polster, J., Steiert, B., & Zhang, J. D. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1796 · Cited by 903
The sequence read archive: explosive growth of sequencing data
Kodama, Y., Shumway, M., Leinonen, R., & on behalf of the International Nucleotide Sequence Database Collaboration (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr854 · Cited by 894
Shen, R., Olshen, A. B., & Ladanyi, M. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp543 · Cited by 849
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
Waring, J., Lindvall, C., & Umeton, R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101822 · Cited by 834
Facing Imbalanced Data--Recommendations for the Use of Performance Metrics
Jeni, L. A., Cohn, J. F., & de la Torre, F. N. (2013) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2013.47 · Cited by 828
More Is Better: Recent Progress in Multi-Omics Data Integration Methods
Huang, S., Chaudhary, K., & Garmire, L. X. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00084 · Cited by 739
Systems biology and multi-omics integration: viewpoints from the metabolomics research community
Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019) · Metabolites · doi:10.3390/metabo9040076 · Cited by 723
The European nucleotide archive
Leinonen, R., Akhtar, R., Birney, E., Bower, L., Cerdeno-Tarraga, A., Cheng, Y., Cleland, I., Faruque, N., Goodgame, N., Gibson, R., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq967 · Cited by 690
Integrated omics: tools, advances and future approaches
Misra, B. B., Langefeld, C., Olivier, M., & Cox, L. A. (2019) · Journal of Molecular Endocrinology · doi:10.1530/jme-18-0055 · Cited by 686
Zhou, B., Xiao, J. F., Tuli, L., & Ressom, H. W. (2011) · Molecular BioSystems · doi:10.1039/c1mb05350g · Cited by 677
Wang, T., Shao, W., Huang, Z., Tang, H., Zhang, J., Ding, Z., & Huang, K. (2021) · Nature Communications · doi:10.1038/s41467-021-23774-w · Cited by 671
A statistical framework for genomic data fusion
Lanckriet, G. R. G., Bie, T. D., Cristianini, N., Jordan, M. I., & Noble, W. S. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth294 · Cited by 667
Muehlematter, U. J., Daniore, P., & Vokinger, K. N. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30292-2 · Cited by 665
Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)
Memon, J., Sami, M., Khan, R. A., & Uddin, M. (2020) · IEEE Access · doi:10.1109/access.2020.3012542 · Cited by 655
Lipidomics: Techniques, Applications, and Outcomes Related to Biomedical Sciences
Yang, K., & Han, X. (2016) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2016.08.010 · Cited by 620
Large-Scale Multi-omic Analysis of COVID-19 Severity
Overmyer, K. A., Shishkova, E., Miller, I. J., Balnis, J., Bernstein, M. N., Peters-Clarke, T. M., Meyer, J. G., Quan, Q., Muehlbauer, L. K., Trujillo, E. A., et al. (2020) · Cell Systems · doi:10.1016/j.cels.2020.10.003 · Cited by 607
Machine learning for email spam filtering: review, approaches and open research problems
Dada, E. G., Bassi, J. S., Chiroma, H., Abdulhamid, S. M., Adetunmbi, A. O., & Ajibuwa, O. (2019) · Heliyon · doi:10.1016/j.heliyon.2019.e01802 · Cited by 604
Medical big data: promise and challenges
Lee, C. H., & Yoon, H. (2017) · Kidney Research and Clinical Practice · doi:10.23876/j.krcp.2017.36.1.3 · Cited by 596
Self-supervised learning for medical image analysis using image context restoration
Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101539 · Cited by 575
Joint and individual variation explained (JIVE) for integrated analysis of multiple data types
Lock, E. F., Hoadley, K. A., Marron, J. S., & Nobel, A. B. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas597 · Cited by 572
Multi-omic and multi-view clustering algorithms: review and cancer benchmark
Rappoport, N., & Ron Shamir, A. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky889 · Cited by 570
Shen, R., Olshen, A. B., & Ladanyi, M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp659 · Cited by 564
MGnify: the microbiome analysis resource in 2020
Mitchell, A. L., Almeida, A., Beracochea, M., Boland, M., Burgin, J., Cochrane, G., Crusoe, M. R., Kale, V., Potter, S. C., Richardson, L. J., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1035 · Cited by 562
Benson, D. A., Karsch‐Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1079 · Cited by 552
Deep learning in clinical natural language processing: a methodical review
Wu, S., Roberts, K. E., Datta, S., Du, J., Ji, Z., Si, Y., Soni, S., Wang, Q., Wei, Q., Xiang, Y., et al. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz200 · Cited by 524
A survey of dimensionality reduction techniquesPreprint
Sorzano, C. Ó. S., Vargas, J., & Pascual-Montano, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.2877 · Cited by 502
Overview of DNA Microarrays: Types, Applications, and Their Future
Bumgarner, R. E. (2013) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb2201s101 · Cited by 501
Targeted RNA sequencing reveals the deep complexity of the human transcriptome
Mercer, T. R., Gerhardt, D. J., Dinger, M. E., Crawford, J., Trapnell, C., Jeddeloh, J. A., Mattick, J. S., & Rinn, J. L. (2011) · Nature Biotechnology · doi:10.1038/nbt.2024 · Cited by 490
A comprehensive survey of anomaly detection techniques for high dimensional big data
Thudumu, S., Branch, P., Jin, J., & Singh, J. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00320-x · Cited by 488
An integrated approach to uncover drivers of cancer
Akavia, U. D., Litvin, O., Kim, J., Sanchez-Garcia, F., Kotliar, D., Causton, H. C., Pochanard, P., Mozes, E., Garraway, L. A., & Pe'er, D. (2010) · Cell · doi:10.1016/j.cell.2010.11.013 · Cited by 484
Zhou, J. Z., He, Z., Yang, Y., Deng, Y., Tringe, S. G., & Alvarez‐Cohen, L. (2015) · mBio · doi:10.1128/mbio.02288-14 · Cited by 484
Pattern discovery and cancer gene identification in integrated cancer genomic data
Mo, Q., Wang, S., Seshan, V. E., Olshen, A. B., Schultz, N., Sander, C., Powers, R. S., Ladanyi, M., & Shen, R. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1208949110 · Cited by 474
Genome sequencing of the sweetpotato whitefly Bemisia tabaci MED/Q
Xie, W., Chen, C., Yang, Z., Guo, L., Yang, X., Wang, D., Chen, M., Huang, J., Wen, Y., Zeng, Y., et al. (2017) · GigaScience · doi:10.1093/gigascience/gix018 · Cited by 473
Multidimensional Scaling: History, Theory and Applications.
Pigden, N., Young, F. W., & Hamer, R. M. (1988) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348396 · Cited by 470
A comparative evaluation of outlier detection algorithms: Experiments and analyses
Domingues, R., Filippone, M., Pietro Michiardi, A., & Zouaoui, J. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.09.037 · Cited by 457
Hugenholtz, P., & Tyson, G. W. (2008) · Nature · doi:10.1038/455481a · Cited by 457
Methods for the integration of multi-omics data: mathematical aspects
Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., & Milanesi, L. (2016) · BMC Bioinformatics · doi:10.1186/s12859-015-0857-9 · Cited by 449
Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework
Chawla, N. V., & Davis, D. A. (2013) · Journal of General Internal Medicine · doi:10.1007/s11606-013-2455-8 · Cited by 440
yaImpute : An R Package for k NN Imputation
Crookston, N. L., & Finley, A. O. (2008) · Journal of Statistical Software · doi:10.18637/jss.v023.i10 · Cited by 437
MOLI: multi-omics late integration with deep neural networks for drug response prediction
Sharifi-Noghabi, H., Zolotareva, O., Collins, C. C., & Ester, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz318 · Cited by 421
Probabilistic model of the human protein-protein interaction network
Rhodes, D. R., Tomlins, S. A., Varambally, S., Mahavisno, V., Barrette, T. R., Sundaram, S. K., Ghosh, D., Pandey, A., & Chinnaiyan, A. M. (2005) · Nature Biotechnology · doi:10.1038/nbt1103 · Cited by 411
Non-coding RNA analysis using the Rfam database
Kalvari, I., Nawrocki, E. P., Argasinska, J., Quinones-Olvera, N., Finn, R., Bateman, A., & Petrov, A. I. (2018) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.51 · Cited by 401
The Application of Deep Learning in Cancer Prognosis Prediction
Zhu, W., Xie, L., Han, J., & Guo, X. (2020) · Cancers · doi:10.3390/cancers12030603 · Cited by 398
Secondary arterial hypertension: when, who, and how to screen?
Rimoldi, S. F., Scherrer, U., & Messerli, F. H. (2014) · European Heart Journal · doi:10.1093/eurheartj/eht534 · Cited by 393
Machine learning, medical diagnosis, and biomedical engineering research - commentary
Foster, K. R., Koprowski, R., & Skufca, J. D. (2014) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-13-94 · Cited by 384
Dimension reduction techniques for the integrative analysis of multi-omics data
Meng, C., Zeleznik, O. A., Thallinger, G., Küster, B., Gholami, A. M., & Culhane, A. C. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbv108 · Cited by 384
Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P., & Green, R. (2019) · Academic Pathology · doi:10.1177/2374289519873088 · Cited by 381
Machine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380
The Handbook of Metabonomics and Metabolomics
Lindon, J. C., Nicholson, J., & Holmes, E. (2007) · Elsevier eBooks · doi:10.1016/b978-0-444-52841-4.x5000-0 · Cited by 374
Discovery of multi-dimensional modules by integrative analysis of cancer genomic data
Zhang, S., Liu, C.-C., Li, W., Shen, H., Laird, P. W., & Zhou, X. J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks725 · Cited by 374
Ensemble classifier for protein fold pattern recognition
Shen, H., & Chou, K. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl170 · Cited by 357
Kovács, G. G. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17020189 · Cited by 346
C-reactive protein, inflammation and coronary heart disease
Shrivastava, A. K., Singh, H. V., Raizada, A., & Singh, S. K. (2014) · The Egyptian Heart Journal · doi:10.1016/j.ehj.2014.11.005 · Cited by 332
Lock, E. F., & Dunson, D. B. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt425 · Cited by 320
Reinforcement learning for intelligent healthcare applications: A survey
Coronato, A., Naeem, M., De Pietro, G., & Paragliola, G. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101964 · Cited by 320
Pérez-Cobas, A. E., Gomez-Valero, L., & Buchrieser, C. (2020) · Microbial Genomics · doi:10.1099/mgen.0.000409 · Cited by 314
A reference human genome dataset of the BGISEQ-500 sequencer
Huang, J., Liang, X., Xuan, Y., Geng, C., Li, Y., Lu, H., Qu, S., Mei, X., Chen, H., Yu, T., et al. (2017) · GigaScience · doi:10.1093/gigascience/gix024 · Cited by 313
Poirion, O., Jing, Z., Chaudhary, K., Huang, S., & Garmire, L. X. (2021) · Genome Medicine · doi:10.1186/s13073-021-00930-x · Cited by 313
Beyond mass spectrometry, the next step in proteomics
Timp, W., & Timp, G. L. (2020) · Science Advances · doi:10.1126/sciadv.aax8978 · Cited by 312
Prospects and challenges of multi-omics data integration in toxicology
Canzler, S., Schor, J., Busch, W., Schubert, K. A., Rolle-Kampczyk, U. E., Seitz, H., Kamp, H., von Bergen�, M., Buesen, R., & Hackermüller, J. (2020) · Archives of Toxicology · doi:10.1007/s00204-020-02656-y · Cited by 300
A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data
Mo, Q., Shen, R., Guo, C., Vannucci, M., Chan, K. S., & Hilsenbeck, S. G. (2017) · Biostatistics · doi:10.1093/biostatistics/kxx017 · Cited by 294
Thomas, T. A., Stefanoni, D., Dzieciątkowska, M., Issaian, A., Nemkov, T. G., Hill, R. C., Francis, R. O., Hudson, K. E., Buehler, P. W., Zimring, J. C., et al. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00606 · Cited by 291
Methods for biological data integration: perspectives and challenges
Gligorijević, V., & Pržulj, N. (2015) · Journal of The Royal Society Interface · doi:10.1098/rsif.2015.0571 · Cited by 289
NEMO: cancer subtyping by integration of partial multi-omic data
Rappoport, N., & Shamir, R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz058 · Cited by 286
Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools
Nicora, G., Vitali, F., Dagliati, A., Geifman, N., & Bellazzi, R. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01030 · Cited by 284
Zhou, Z., Chawla, N. V., Jin, Y., & Williams, G. J. (2014) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2014.2350953 · Cited by 283
Integrative Methods and Practical Challenges for Single-Cell Multi-omics
Ma, A., McDermaid, A., Xu, J., Chang, Y., & Ma, Q. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.02.013 · Cited by 280
Novel advances in shotgun lipidomics for biology and medicine
Wang, M., Wang, C., Han, R. H., & Han, X. (2015) · Progress in Lipid Research · doi:10.1016/j.plipres.2015.12.002 · Cited by 274
60 years ago, Francis Crick changed the logic of biology
Cobb, M. A. (2017) · PLoS Biology · doi:10.1371/journal.pbio.2003243 · Cited by 274
On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data
Schwarz, D. F., König, I. R., & Ziegler, A. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq257 · Cited by 269
Painting a new picture of personalised medicine for diabetes
McCarthy, M. I. (2017) · Diabetologia · doi:10.1007/s00125-017-4210-x · Cited by 266
Fundamentals of Clinical Data Science
Kubben, P., Dumontier, M., & Dekker, A. L. A. J. (2018) · Springer eBooks · doi:10.1007/978-3-319-99713-1 · Cited by 258
Criteria for the use of omics-based predictors in clinical trials
McShane, L. M., Cavenagh, M. M., Lively, T., Eberhard, D. A., Bigbee, W. L., Williams, P. M., Mesirov, J. P., Polley, M. C., Kim, K. Y., Tricoli, J. V., et al. (2013) · Nature · doi:10.1038/nature12564 · Cited by 250
Young, J., Modat, M., Cardoso, M. J., Mendelson, A., Cash, D., & Ourselin, S. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.05.004 · Cited by 249
Bayesian correlated clustering to integrate multiple datasets
Kirk, P., Griffin, J. E., Savage, R. S., Ghahramani, Z., & Wild, D. L. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts595 · Cited by 236
Liquid chromatography mass spectrometry-based proteomics: Biological and technological aspects
Karpievitch, Y. V., Polpitiya, A. D., Anderson, G., Smith, R. D., & Dabney, A. R. (2010) · The Annals of Applied Statistics · doi:10.1214/10-aoas341 · Cited by 234
Wu, D., Wang, D., Zhang, M. Q., & Gu, J. (2015) · BMC Genomics · doi:10.1186/s12864-015-2223-8 · Cited by 233
Recent advances of deep learning in bioinformatics and computational biology
Tang, B., Pan, Z., Yin, K., & Khateeb, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00214 · Cited by 233
Mamoshina, P., Volosnikova, M., Ozerov, I. V., Lane, E., Skibina, E., Cortese, F., & Zhavoronkov, A. A. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00242 · Cited by 231
A post-genomic challenge: learning to read patterns of protein synthesis
Abbott, A. (1999) · Nature · doi:10.1038/45350 · Cited by 227
Integrative methods for analyzing big data in precision medicine
Gligorijević, V., Malod‐Dognin, N., & Pržulj, N. (2015) · PROTEOMICS · doi:10.1002/pmic.201500396 · Cited by 226
A systematic comparison of supervised classifiers
Amancio, D. R., Comin, C. H., Casanova, D., Travieso, G., Bruno, O. M., Rodrigues, F. A., & da Fontoura Costa, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094137 · Cited by 225
Machine learning in cybersecurity: A review
Handa, A., Sharma, A., & Shukla, S. K. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1306 · Cited by 225
Zhang, L., Dong, X., Lee, M., Maslov, A. Y., Wang, T., & Vijg, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902510116 · Cited by 222
High-Sensitivity C-Reactive Protein as Cardiovascular Risk Marker in Patients with Diabetes Mellitus
Pfützner, A., & Forst, T. M. (2006) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2006.8.28 · Cited by 221
Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data
Ma, B., Meng, F., Ge, Y., Yan, H., Chai, B., & Song, F. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103761 · Cited by 221
Liew, A. W., Law, N.-F., & Yan, H. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbq080 · Cited by 218
Fast protein classification with multiple networks
Tsuda, K., Shin, H., & Schölkopf, B. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1110 · Cited by 218
A Selective Review of Multi-Level Omics Data Integration Using Variable Selection
Wu, C., Zhou, F., Ren, J., Li, X., Jiang, Y., & Ma, S. (2019) · Microarrays · doi:10.3390/ht8010004 · Cited by 218
Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology
Jamil, I. N., Remali, J., Azizan, K. A., Muhammad, N. A. N., Arita, M., Goh, H., & Aizat, W. M. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00944 · Cited by 218
Hamamoto, R., Suvarna, K., Yamada, M., Kobayashi, K., Shinkai, N., Miyake, M., Takahashi, M., Jinnai, S., Shimoyama, R., Sakai, A., et al. (2020) · Cancers · doi:10.3390/cancers12123532 · Cited by 207
Zhu, J., Sova, P., Xu, Q., Dombek, K. M., Xu, E. Y., Vu, H., Tu, Z., Brem, R. B., Bumgarner, R. E., & Schadt, E. E. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001301 · Cited by 205
Systems Biology of Metabolism: A Driver for Developing Personalized and Precision Medicine
Nielsen, J. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.02.002 · Cited by 204
Zhang, L., Lv, C., Jin, Y., Cheng, G., Fu, Y., Yuan, D., Tao, Y., Guo, Y., Ni, X., & Shi, T. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00477 · Cited by 203
Prognostic and Predictive Biomarkers: Tools in Personalized Oncology
Nalejska, E., Mączyńska, E., & Lewandowska, M. A. (2014) · Molecular Diagnosis & Therapy · doi:10.1007/s40291-013-0077-9 · Cited by 202
Precision Oncology: The Road Ahead
Senft, D., Leiserson, M. D. M., Ruppin, E., & Ronai, Z. A. (2017) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2017.08.003 · Cited by 199
Machine learning and systems genomics approaches for multi-omics data
Lin, E., & Lane, H. (2017) · Biomarker Research · doi:10.1186/s40364-017-0082-y · Cited by 197
Ding, M. Q., Chen, L., Cooper, G. F., Young, J. D., & Lu, X. (2017) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-17-0378 · Cited by 195
Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources
Eicher, T., Kinnebrew, G. H., Patt, A. C., Spencer, K., Ying, K. L., Ma, Q., Machiraju, R., & Mathé, E. A. (2020) · Metabolites · doi:10.3390/metabo10050202 · Cited by 191
Mass Spectrometry Based Lipidomics: An Overview of Technological Platforms
Köfeler, H. C., Fauland, A., Rechberger, G. N., & Trötzmüller, M. (2012) · Metabolites · doi:10.3390/metabo2010019 · Cited by 187
Big Data and Biomedical Informatics: A Challenging Opportunity
Bellazzi, R. (2014) · Yearbook of Medical Informatics · doi:10.15265/iy-2014-0024 · Cited by 184
Speicher, N. K., & Pfeifer, N. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv244 · Cited by 183
A novel approach for data integration and disease subtypingPreprint
Nguyen, T., Tagett, R., Diaz, D., & Drăghici, S. (2017) · Genome Research · doi:10.1101/gr.215129.116 · Cited by 183
Making multi-omics data accessible to researchers
Conesa, A., & Beck, S. (2019) · Scientific Data · doi:10.1038/s41597-019-0258-4 · Cited by 183
Comparing machine learning classifiers in potential distribution modelling
Lorena, A. C., Jacintho, L. F. O., de Siqueira, M. F., Giovanni, R. D., Lohmann, L. G., de Carvalho, A. C. P. L. F., & Yamamoto, M. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.10.031 · Cited by 180
Zhou, Y., Hou, Y., Shen, J., Mehra, R., Kallianpur, A., Culver, D. A., Gack, M. U., Farha, S., Zein, J., Comhair, S., et al. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000970 · Cited by 180
Lightbody, G., Haberland, V., Browne, F., Taggart, L., Zheng, H., Parkes, E., & Blayney, J. K. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby051 · Cited by 173
Frontiers of high-throughput metabolomics
Zampieri, M., Sekar, K., Zamboni, N., & Sauer, U. (2017) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2016.12.006 · Cited by 171
Applications and analysis of targeted genomic sequencing in cancer studies
Bewicke‐Copley, F., Kumar, E. A., Palladino, G., Korfi, K., & Wang, J. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.10.004 · Cited by 164
DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines
Li, M., Wang, Y., Zheng, R., Shi, X. M., Li, Y., Wu, F., & Wang, J. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2919581 · Cited by 162
A versatile ultra-high performance LC-MS method for lipid profiling
Knittelfelder, O. L., Weberhofer, B. P., Eichmann, T. O., Kohlwein, S. D., & Rechberger, G. N. (2014) · Journal of Chromatography B · doi:10.1016/j.jchromb.2014.01.011 · Cited by 161
Gupta, Y., Lama, R. K., Kwon, G., & Initiative, A. D. N. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00072 · Cited by 161
Bzdok, D. (2017) · HAL · Cited by 160
Meta-analytic support vector machine for integrating multiple omics data
Kim, S., Jhong, J.-H., Lee, J., & Koo, J.-Y. (2017) · BioData Mining · doi:10.1186/s13040-017-0126-8 · Cited by 159
Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification
Nam, H., Chung, B. C., Kim, Y.-H., Lee, K., & Lee, D. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp558 · Cited by 158
GlyTouCan: an accessible glycan structure repository
Tiemeyer, M., Aoki, K., Paulson, J. C., Cummings, R. D., York, W. S., Karlsson, N. G., Lisacek, F., Packer, N. H., Campbell, M. P., Aoki, N. P., et al. (2017) · Glycobiology · doi:10.1093/glycob/cwx066 · Cited by 158
Classical Statistics and Statistical Learning in Imaging Neuroscience
Bzdok, D. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00543 · Cited by 158
Multi-omics integration—a comparison of unsupervised clustering methodologies
Tini, G., Marchetti, L., Priami, C., & Scott‐Boyer, M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx167 · Cited by 157
Challenges in the Integration of Omics and Non-Omics Data
de Maturana, E. L., Alonso, L., Moreno, P. A., Martín‐Antoniano, I. A., Pineda, S., Piorno, L., Calle, M. L., & Malats, N. (2019) · Genes · doi:10.3390/genes10030238 · Cited by 157
Zhao, Q., Shi, X., Xie, Y., Huang, J., Shia, B., & Ma, S. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbu003 · Cited by 154
DNA Data Bank of Japan (DDBJ) for genome scale research in life science
Tateno, Y. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.27 · Cited by 149
Xu, X., Liang, T., Zhu, J., Zheng, D., & Sun, T. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.02.100 · Cited by 149
moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets
Meng, C., Helm, D., Frejno, M., & Küster, B. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00824 · Cited by 147
Hampel, H., O’Bryant, S. E., Durrleman, S., Younesi, E., Rojkova, K., Escott-Price, V., Corvol, J.-C., Broich, K., Dubois, B., Lista, S., et al. (2017) · Climacteric · doi:10.1080/13697137.2017.1287866 · Cited by 147
PINSPlus: a tool for tumor subtype discovery in integrated genomic data
Nguyen, H., Shrestha, S., Drăghici, S., & Nguyen, T. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty1049 · Cited by 145
Research works printed but not linked: 3
- Clarivate Analytics, 2020, Web of science [v.5.35] - web of science core collection basic search [WWW Document], Web Sci.
- Tipping, 2001, Sparse bayesian learning and the relevance vector machine, J. Mach. Learn. Res., 1, 211
- Wang, 2020, MORONET: multi-omics integration via graph convolutional networks for biomedical data classification, bioRxiv
Cited by
Sorted by citations
Microglia states and nomenclature: A field at its crossroads
Paolicelli, R. C., Sierra, A., Stevens, B., Tremblay, M.-E., Aguzzi, A., Ajami, B., Amit, I., Audinat, E., Bechmann, I., Bennett, M., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.10.020 · Cited by 1,973
Applications of multi‐omics analysis in human diseases
Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G. (2023) · MedComm · doi:10.1002/mco2.315 · Cited by 545
AI for life: Trends in artificial intelligence for biotechnology
Holzinger, A., Keiblinger, K., Holub, P., Zatloukal, K., & Müller, H. (2023) · New Biotechnology · doi:10.1016/j.nbt.2023.02.001 · Cited by 438
Machine learning for multi-omics data integration in cancer
Cai, Z., Poulos, R. C., Liu, J., & Zhong, Q. (2022) · iScience · doi:10.1016/j.isci.2022.103798 · Cited by 366
Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320
Metadata
- Authors
- Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E.
- Affiliations
- University of Dundee (GB)
- Corresponding author
- Jefferson, Emily
- Venue
- Biotechnology Advances
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 49
- Pages
- 107739
- ISSN
- 0734-9750 (ISSN-L)
- OpenAlex
- W3139601120
- Semantic Scholar
- adb2c80911fbb5c273279afa79f6a766700a1040
- Cited by
- 903
- References
- 363
- Citations by year
- 2026: 186 · 2025: 239 · 2024: 227 · 2023: 148 · 2022: 89 · 2021: 14
- FWCI
- 13.04
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Biotechnology Advances (publishedVersion)
- PubMed (publishedVersion)
- Discovery Research Portal (University of Dundee) (publishedVersion, cc-by-nc-nd)
- Funders
- Horizon 2020
- University of Dundee (16/136/102)
- Horizon 2020 Framework Programme (633983)
- National Institute for Health Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- multi-omics integration, machine learning, biomarker discovery, disease prediction, patient stratification, precision medicine, systems biology, genomics, proteomics, metabolomics, predictive algorithms
- MeSH terms
- Machine Learning, Algorithms, Humans, Proteomics, Systems Biology, Metabolomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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