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
AI snapshot
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.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
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
The cited sources support only part of this claim — read each source’s verdict below.
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.
- 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.
- 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
plain- 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.
- 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)
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.
- Supported
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
boosted- 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.
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.
- 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.
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
plain- 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.
None of the sources cited for this claim have been checked yet.
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
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.
None of the sources cited for this claim have been checked yet.
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
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.
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
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.
None of the sources cited for this claim have been checked yet.
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
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.
- Probably not a substantial claim. 56
- It may state two separate claims as one. 41
- The quote only partly states this claim. 1
None of the sources cited for this claim have been checked yet.
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
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
“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.
- 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 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.
- 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.
Probably not a substantial claim.
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.
- 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.
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.
- 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
“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.
- 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
“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.
- 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
“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
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
“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
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
“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
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
“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
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
“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
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
“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
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.
None of the sources cited for this claim have been checked yet.
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
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
“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?
hedgedconditional- 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. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
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
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
“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
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
“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
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.
None of the sources cited for this claim have been checked yet.
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
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.
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
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
“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
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
“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
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.
None of the sources cited for this claim have been checked yet.
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
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
“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
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
“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
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
Items 101–200 of 960
Yarbro, J. M., Shrestha, H. K., Wang, Z., Zhang, X., Zaman, M., Chu, M., Wang, X., Yu, G., & Peng, J. (2025) · Molecular Neurodegeneration · doi:10.1186/s13024-025-00874-5 · Cited by 43
Clark, C., Rabl, M., Dayon, L., & Popp, J. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1065904 · Cited by 45
Li, R., Chen, S., Matsumoto, H., Gouda, M., Gafforov, Y., Wang, M., & Liu, Y. (2023) · aBIOTECH · doi:10.1007/s42994-023-00126-4 · Cited by 42
Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation
Xu, Y., Cao, L., Chen, Y., Zhang, Z., Liu, W., Li, H., Ding, C., Pu, J., Qian, K., & Xu, W. L. (2024) · Small Methods · doi:10.1002/smtd.202400305 · Cited by 42
Cao, S., Wei, Y., Xu, H., Weng, J., Qi, T., Yu, F., Liu, S., Xiong, A., Liu, P., & Zeng, H. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1202436 · Cited by 42
Goodrich, J. A., Wang, H., Jia, Q., Stratakis, N., Zhao, Y., Maître, L., Bustamante, M., Vafeiadi, M., Aung, M. T., Andrušaitytė, S., et al. (2024) · Environment International · doi:10.1016/j.envint.2024.108930 · Cited by 41
Harnessing artificial intelligence for engineering extracellular vesicles
Lü, H., Zhang, J., Shen, T., Jiang, W., Liu, H., & Su, J. (2025) · Extracellular Vesicles and Circulating Nucleic Acids · doi:10.20517/evcna.2025.35 · Cited by 44
Vilne, B., Ķibilds, J., Siksna, I., Lazda, I., Valciņa, O., & Krūmiņa, A. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.627892 · Cited by 40
Yoon, J. H., Lee, H., Kwon, D., Lee, D., Lee, S., Cho, E., Kim, J., & Kim, D. (2024) · Brain Communications · doi:10.1093/braincomms/fcae265 · Cited by 43
Wang, N., Zhou, X., Zhang, T., Jian, W., Sun, Z., Qi, P., Feng, Y., Liu, H., Liu, L., & Song, Y. (2025) · Food Research International · doi:10.1016/j.foodres.2025.116034 · Cited by 40
A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases
Pratella, D., Ait-El-Mkadem, S., Bannwarth, S., Paquis‐Flucklinger, V., & Bottini, S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms221910891 · Cited by 40
Son, J., Sohn, Y. J., Baritugo, K., Jo, S. Y., Song, H. M., & Park, S. J. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2022.108070 · Cited by 40
Yu, Y., Zhang, Y., Liu, Y., Lv, M., Wang, Z., Wen, L.-L., & Li, A. (2023) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2023.131243 · Cited by 39
Xiao, X., Fu, L., Zhang, Z., Tu, Z., Shen, N., & Fan, S. (2025) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c08522 · Cited by 39
Bobo, W. V., Ommeren, B. V., & Athreya, A. P. (2022) · Expert Review of Clinical Pharmacology · doi:10.1080/17512433.2022.2112949 · Cited by 39
Emerging Microorganisms and Infectious Diseases: One Health Approach for Health Shared Vision
Ristori, M. V., Guarrasi, V., Soda, P., Petrosillo, N., Gurrieri, F., Longo, U. G., Ciccozzi, M., Riva, E., & Angeletti, S. (2024) · Genes · doi:10.3390/genes15070908 · Cited by 38
Artificial intelligence-guided design of lipid nanoparticles for mRNA delivery
Su, K., Qiu, J., Xu, T., & Liu, S. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.11.029 · Cited by 39
Jiang, F., Zhou, H., & Shen, H. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.925695 · Cited by 37
DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways
Cai, Z., Poulos, R. C., Aref, A., Robinson, P. J., Reddel, R. R., & Zhong, Q. (2024) · Cancer Research Communications · doi:10.1158/2767-9764.crc-24-0285 · Cited by 36
She, H., Du, Y., Du, Y., Tan, L., Shunxin, Y., Luo, X., Li, Q., Xiang, X., Lu, H., Hu, Y., et al. (2023) · BMC Anesthesiology · doi:10.1186/s12871-023-02317-4 · Cited by 36
Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review
Khanna, N. N., Singh, M., Maindarkar, M. A., Kumar, A., Johri, A. M., Mantella, L. E., Laird, J. R., Paraskevas, K. I., Ruzsa, Z., Singh, N., et al. (2023) · Journal of Korean Medical Science · doi:10.3346/jkms.2023.38.e395 · Cited by 36
Molecular insights fast-tracked: AI in biosynthetic pathway research
Liao, L., Xie, M., Zheng, X., Zhou, Z., Deng, Z., & Gao, J. (2025) · Natural Product Reports · doi:10.1039/d4np00003j · Cited by 36
Current Status of Omics in Biological Quality Elements for Freshwater Biomonitoring
Machuca‐Sepúlveda, J., Miranda, J., Lefin, N., Pedroso, A., Beltrán, J. F., & Farías, J. G. (2023) · Biology · doi:10.3390/biology12070923 · Cited by 35
Li, H., Zhao, X., Li, X., Liang, J., Qin, S., Li, J., Zhang, A., Xu, L., Tang, D., & Li, F. (2025) · Food Chemistry · doi:10.1016/j.foodchem.2025.144840 · Cited by 35
Zhang, W., Huang, F., Ding, X., Qin, J., Wang, W., & Luo, L. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2024.167176 · Cited by 34
Plant-pathogen interactions: making the case for multi-omics analysis of complex pathosystems
Balotf, S., Wilson, R., Hemmati, R., Eshaghi, M., Wilson, C. R., & Mur, L. A. J. (2025) · Stress Biology · doi:10.1007/s44154-025-00260-7 · Cited by 34
A critical review of machine-learning for “multi-omics” marine metabolite datasets
Manochkumar, J., Cherukuri, A. K., Kumar, R. S., Almansour, A. I., Ramamoorthy, S., & Efferth, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107425 · Cited by 33
Meta-learning in Healthcare: A Survey
Rafiei, A., Moore, R., Jahromi, S., Hajati, F., & Kamaleswaran, R. (2024) · SN Computer Science · doi:10.1007/s42979-024-03166-9 · Cited by 33
Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review
Guha, A., Shah, V., Nahle, T., Singh, S., Kunhiraman, H. H., Shehnaz, F., Nain, P., Makram, O. M., Mahmoudi, M., Al-Kindi, S. G., et al. (2025) · Current Cardiology Reports · doi:10.1007/s11886-025-02215-w · Cited by 33
Integrating multi-omics and machine learning for disease resistance prediction in legumes
Mohamedikbal, S., Al-Mamun, H. A., Bestry, M., Batley, J., & Edwards, D. (2025) · Theoretical and Applied Genetics · doi:10.1007/s00122-025-04948-2 · Cited by 33
Ecosystem-specific microbiota and microbiome databases in the era of big data
Lobanov, V. P., Gobet, A., & Joyce, A. (2022) · Environmental Microbiome · doi:10.1186/s40793-022-00433-1 · Cited by 33
Explainable and programmable hypergraph convolutional network for imaging genetics data fusion
Bi, X.-A., Luo, S., Jiang, S., Wang, Y., Xing, Z., & Xu, L. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101950 · Cited by 33
Can digital twin efforts shape microorganism-based alternative food?
Helmy, M., Elhalis, H., Rashid, M. M., & Selvarajoo, K. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103115 · Cited by 32
Xu, D., Liu, B., Wang, J., & Zhang, Z. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.998298 · Cited by 32
Artificial intelligence accelerates multi-modal biomedical process: A Survey
Li, J., Han, X., Qin, Y., Tan, F., Chen, Y., Wang, Z., Song, H., Zhou, X., Zhang, Y., Hu, L., et al. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126720 · Cited by 32
Li, K., Desai, R., Scott, R. T., Steele, J. R., Machado, M., Demharter, S., Hoarfrost, A., Braun, J. L., Fajardo, V. A., Sanders, L., et al. (2023) · npj Microgravity · doi:10.1038/s41526-023-00337-5 · Cited by 31
Multi-Omics Integration in Nephrology: Advances, Challenges, and Future Directions
Saliba, A., Du, Y., Feng, T., & Garmire, L. X. (2024) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2025.151584 · Cited by 32
Fan, Y., Kao, C., Yang, F., Wang, F., Yin, G., Wang, Y., He, Y., Ji, J., & Liu, L. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.899900 · Cited by 31
Liu, X., Tang, H., Zhou, Q., Zeng, Y.-L., Chen, D., Xu, H., Li, Y., Tan, B., & Qian, J. (2023) · World Journal of Gastroenterology · doi:10.3748/wjg.v29.i2.272 · Cited by 31
MLOmics: Cancer Multi-Omics Database for Machine Learning
Yang, Z., Kotoge, R., Piao, X., Chen, Z., Zhu, L., Gao, P., Matsubara, Y., Sakurai, Y., & Sun, J. (2025) · Scientific Data · doi:10.1038/s41597-025-05235-x · Cited by 31
Wang, H., Lin, K., Zhang, Q., Shi, J., Song, X., Wu, J., Zhao, C., & He, K. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae159 · Cited by 30
A review of Ribosome profiling and tools used in Ribo-seq data analysis
Limbu, M. S., Xiong, T., & Wang, S. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.051 · Cited by 30
Harnessing omics data for drug discovery and development in ovarian aging
Zhang, F., Zhu, M., Chen, Y., Wang, G., Yang, H., Lu, X., Li, Y., Chang, H., Wu, Y., Ma, Y., et al. (2025) · Human Reproduction Update · doi:10.1093/humupd/dmaf002 · Cited by 30
Next-generation probiotics and engineered BEVs for precision therapeutics in osteoporosis
Wei, J., Ding, W., Song, K., Zhang, Y., Luo, Q., & Qi, C. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1581971 · Cited by 30
Human Umbilical Vein Endothelial Cells (HUVECs) in Pharmacology and Toxicology: A Review
Cao, Y. (2025) · Journal of Applied Toxicology · doi:10.1002/jat.4885 · Cited by 34
Li, H., Tao, X., Liang, T., Jiang, J., Zhu, J., Wu, S., Chen, L., Zhang, Z., Zhou, C., Sun, X., et al. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1063633 · Cited by 30
El-Hack, M. E. A., Allam, A. A., Aldhalmi, A. K., Kamal, M., Arif, M. A., Alawam, A. S., Rudayni, H. A., Taha, A. E.-S., Swelum, A. A., Elolimy, A. A., et al. (2025) · Frontiers in Veterinary Science · doi:10.3389/fvets.2025.1594749 · Cited by 29
Zhou, Y., Ping, X., Guo, Y., Heng, B. C., Wang, Y., Meng, Y., Jiang, S., Wei, Y., Lai, B., Zhang, X., et al. (2023) · Advanced Materials · doi:10.1002/adma.202210637 · Cited by 29
Shi, Y., Zhang, C., Pan, S., Chen, Y., Miao, X., He, G., Wu, Y., Ye, H., Weng, C., Zhang, H., et al. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1290746 · Cited by 29
Cancer-associated fibroblast-secreted FGF7 as an ovarian cancer progression promoter
Feng, S., Ding, B., Dai, Z., Yin, H., Ding, Y., Liu, S., Zhang, K., Lin, H. R., Xiao, Z., & Shen, Y. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05085-y · Cited by 32
Ren, Y., Wu, C., Zhou, H., Hu, X., & Miao, Z. (2024) · Plant Communications · doi:10.1016/j.xplc.2024.101002 · Cited by 28
Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
Papadaki, E., Kakkos, I., Vlamos, P., Πετροπούλου, Ο., Miloulis, S.-T., Παλαμάς, Σ., & Vrahatis, A. G. (2024) · Applied Sciences · doi:10.3390/app15010329 · Cited by 28
Multi-modal intermediate integrative methods in neuropsychiatric disorders: A review
Wang, Y., Tang, S., Ma, R., Zamit, I., Wei, Y., & Pan, Y. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.008 · Cited by 28
Bartsch, M., Hahn, A., & Berkemeyer, S. (2023) · Metabolites · doi:10.3390/metabo13121182 · Cited by 28
Big data and AI for gender equality in health: bias is a big challenge
Joshi, A. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1436019 · Cited by 27
Progress in multi-omics studies of osteoarthritis
Wei, Y., He, Q., Zhang, X., Wang, J., Yan, H., Xiao, N., Zeng, S., Chen, B., Yang, Q., Lu, H., et al. (2025) · Biomarker Research · doi:10.1186/s40364-025-00732-y · Cited by 27
The Role of microRNAs in Lung Cancer: Mechanisms, Diagnostics and Therapeutic Potential
Bartoszewska, E., Misiąg, P., Czapla, M., Rakoczy, K., Tomecka, P., Filipski, M., Wawrzyniak-Dzierżek, E., & Choromańska, A. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26083736 · Cited by 27
Li, J., Ding, C., Liu, D., Chen, L., & Jiang, J. (2025) · Digital Discovery · doi:10.1039/d5dd00072f · Cited by 31
Advances in sequencing technologies for amyotrophic lateral sclerosis research
Udine, E., Jain, A., & van Blitterswijk, M. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00593-1 · Cited by 27
Dig information of nanogenerators by machine learning
Zhang, J., Yu, Y., Zhang, L., Chen, J., Wang, X., & Wang, X. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108656 · Cited by 27
Recent advances of pathomics in colorectal cancer diagnosis and prognosis
Wu, Y., Li, Y., Xiong, X., Liu, X., Lin, B., & Xu, B. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1094869 · Cited by 27
Samadishadlou, M., Rahbarghazi, R., Piryaei, Z., Esmaeili, M., Avcı, Ç. B., Bani, F., & Kavousi, K. (2023) · Cardiovascular Diabetology · doi:10.1186/s12933-023-01957-7 · Cited by 26
Multimodal Learning for Multi-omics: A Survey
Tabakhi, S., Suvon, M. N. I., Ahadian, P., & Lu, H. (2022) · World Scientific Annual Review of Artificial Intelligence · doi:10.1142/s2811032322500047 · Cited by 27
Artificial Intelligence for Risk Assessment on Primary Prevention of Coronary Artery Disease
Chen, S., Loguercio, S., Chen, K., Lee, S. E., Park, J., Liu, S., Sadaei, H. J., & Torkamani, A. (2023) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-023-00731-4 · Cited by 26
Gong, G., Wu, B., Liu, L., Li, J., & He, M. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108432 · Cited by 25
Artificial Intelligence and the Practice of Neurology in 2035
Jones, D. T., & Kerber, K. A. (2022) · Neurology · doi:10.1212/wnl.0000000000013200 · Cited by 25
Wang, Z., Kim, W., Wang, Y., Yakubovich, E., Dong, C., Trail, F., Townsend, J. P., & Yarden, O. (2023) · Frontiers in Fungal Biology · doi:10.3389/ffunb.2023.1214537 · Cited by 25
A quantitative benchmark of neural network feature selection methods for detecting nonlinear signals
Passemiers, A., Folco, P., Raimondi, D., Birolo, G., Moreau, Y., & Fariselli, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-82583-5 · Cited by 26
Tree smoothing: Post-hoc regularization of tree ensembles for interpretable machine learning
Pfeifer, B., Gevaert, A., Loecher, M., & Holzinger, A. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121564 · Cited by 24
Machine learning for precision diagnostics of autoimmunity
Kruta, J., Carapito, R., Trendelenburg, M., Martin, T., Rizzi, M., Voll, R. E., Cavalli, A., Natali, E. N., Meier, P., Stawiski, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-76093-7 · Cited by 24
Promoting lignocellulosic biorefinery by machine learning: progress, perspectives and challenges
Huang, X., Zhang, X., Xing, L., Huang, S., Zhang, C., Hu, X.-C., & Liu, C. (2025) · Bioresource Technology · doi:10.1016/j.biortech.2025.132434 · Cited by 24
Wang, J., & Chao, J. (2025) · Biomedicines · doi:10.3390/biomedicines13092299 · Cited by 25
Navigating Transition Metal‐Dependent Cell Death: Mechanisms, Crosstalk, and Future Directions
Song, Q., Yang, Y., & Yang, L. (2025) · Advanced Science · doi:10.1002/advs.202501974 · Cited by 24
Wang, S., Wang, S., & Wang, Z. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2022.1109365 · Cited by 24
Peng, Y., Yin, D., Li, X., Wang, K., Li, W., Huang, Y., Liu, X., Ren, Z., Yang, X., Zhang, Z., et al. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-04572-x · Cited by 24
Chemical safety and the exposome
Karlsson, O. (2023) · Emerging contaminants · doi:10.1016/j.emcon.2023.100225 · Cited by 24
Liang, H., Luo, H., Sang, Z., Jia, M., Jiang, X., Wang, Z., Yao, X., & Cong, S. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3439713 · Cited by 24
Advances in proteomic profiling of pediatric kidney diseases
Cummins, T. D., Korte, E. A., Bhayana, S., Merchant, M. L., Barati, M. T., Smoyer, W. E., & Klein, J. B. (2022) · Pediatric Nephrology · doi:10.1007/s00467-022-05497-2 · Cited by 23
Zhong, Y., Peng, Y., Lin, Y., Chen, D., Zhang, H., Zheng, W., Chen, Y., & Wu, C. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02173-9 · Cited by 23
Non‐Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning
Weng, R., Xu, Y., Gao, X., Cao, L., Su, J., Yang, H., Li, H., Ding, C., Pu, J., Zhang, M., et al. (2024) · Advanced Science · doi:10.1002/advs.202405580 · Cited by 22
Artificial Intelligence Can Predict Personalized Immunotherapy Outcomes in Cancer
Huang, L., Wu, X., You, J., Jin, Z., He, W., Sun, J., Shen, H., Liu, X., Yue, X., Cai, W., et al. (2025) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-24-1270 · Cited by 22
Integrating miRNA profiling and machine learning for improved prostate cancer diagnosis
Singh, S., Pathak, A. K., Kural, S., Kumar, L., Bhardwaj, M. G., Yadav, M., Trivedi, S., Das, P. K., Gupta, M., & Jain, G. (2025) · Scientific Reports · doi:10.1038/s41598-025-99754-7 · Cited by 22
A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets
Han, E., Kwon, H., & Jung, I. (2025) · BMC Genomics · doi:10.1186/s12864-025-11925-y · Cited by 22
Zhou, J., Jiang, Y., Yu, M., Wang, M., Li, Y., Ji, D., Zhan, J., & Zhang, H. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01324-1 · Cited by 22
Liu, H., Xing, K., Jiang, Y., Liu, Y., Wang, C., & Ding, X. (2022) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.2c03339 · Cited by 22
Mouse tissue glycome atlas 2022 highlights inter-organ variation in major N-glycan profiles
Otaki, M., Hirane, N., Natsume‐Kitatani, Y., Itoh, M. N., Shindo, M., Kurebayashi, Y., & Nishimura, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-21758-4 · Cited by 22
Pathak, R. K., & Kim, J. (2022) · Frontiers in Veterinary Science · doi:10.3389/fvets.2022.1008728 · Cited by 22
ProgCAE: a deep learning-based method that integrates multi-omics data to predict cancer subtypes
Liu, Q., & Song, K. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad196 · Cited by 22
Biomarker and genomic analyses reveal molecular signatures of non-cardioembolic ischemic stroke
Ding, L., Liu, Y., Meng, X., Jiang, Y., Lin, J., Cheng, S., Xu, Z., Zhao, X., Li, H., Wang, Y., et al. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01465-w · Cited by 22
Lessons and Applications of Omics Research in Diabetes Epidemiology
Yu, G., Tam, H. C. H., Huang, C., Shi, M., Lim, C. K. P., Chan, J. C. N., & Ronald, C. W. (2024) · Current Diabetes Reports · doi:10.1007/s11892-024-01533-7 · Cited by 23
Zhou, B., Zhou, N., Liu, Y., Dong, E., Peng, L., Wang, Y., Yang, L., Suo, H., & Tao, J. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1309992 · Cited by 22
Chen, J.-Y., Chen, W.-J., Zhu, Z.-Y., Shi, X., Huang, L.-L., Tan, W.-Q., Zhang, Y., & Zhao, Y. (2025) · PLoS ONE · doi:10.1371/journal.pone.0313494 · Cited by 22
Application of spatial and single-cell omics in tumor immunotherapy biomarkers
Zhang, C., Feng, H.-R., Zhu, J., & Hong, W. (2025) · LabMed discovery. · doi:10.1016/j.lmd.2025.100076 · Cited by 21
Machine learning-powered multi-omics for food microbiology and smarter food safety
Bansal, S., Rodriguez, C. Z., Thompson‐Witrick, K. A., Wang, Y., Taft, D. H., & Zhang, B. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.105145 · Cited by 21
Olawade, D. B., Kade, A., Egbon, E., Usman, S. O., Fapohunda, O., Ijiwade, J. O., & Ogbonna, C. E. (2025) · Molecular Genetics and Genomics · doi:10.1007/s00438-025-02314-x · Cited by 21
Jiang, S., Locatello, L. G., Maggiore, G., & Gallo, O. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12154958 · Cited by 21
Wu, X., & Jia, W. (2023) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2023.2248633 · Cited by 21
Role of electrochemical nanobiosensors in colorectal cancer precision medicine
Cruz‐Pacheco, A. F., Echeverri, D., & Orozco, J. (2023) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2023.117467 · Cited by 21
Sayyad, P. W., Park, S., & Ha, T. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108297 · Cited by 21
A personalized probabilistic approach to ovarian cancer diagnostics
Ban, D., Housley, S. N., Matyunina, L. V., McDonald, L. D., Bae-Jump, V. L., Benigno, B. B., Skolnick, J., & McDonald, J. F. (2024) · Gynecologic Oncology · doi:10.1016/j.ygyno.2023.12.030 · Cited by 21
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
