The DOI matches its Crossref record; 137 of the 148 references it cites were found in a registry or an index; none of the 80 sources its claims cite is checked for support yet.
Missing data in multi-omics integration: Recent advances through artificial intelligence
Open accessAbstract
Biological systems function through complex interactions between various ‘omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across ‘omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more ‘omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review surveys recent AI/ML multi-omics integration methods that explicitly handle partially observed data, either by joint imputation within the model or by optimization masking of missing components. It also summarizes traditional missing-data workflows and discusses limitations, especially sample-size demands, limited coverage of mass-spectrometry omics, and interpretability.
Key findings
- Missing data are pervasive in omics: proteomics can have 20–50% of possible peptide values unquantified, and the human proteome project estimates ~20% of genes yield protein products not detected by mass spectrometry.
- The review groups missing-data-robust integration methods into joint-imputation approaches (e.g., FBM, iMODA, MOFA/MOFA+, BIDIFAC+) and optimization-masking approaches (e.g., TiMEG, COMBI, MVAE, DeepMF, MvNE, DeepIMV, JACA, SUMO, NEMO, MSNE, MONET), across early, middle, and late integration categories.
- Reported performance varies with missingness: FBM prediction RMSE increased with missing percentage, most prominently when both gene expression and methylation had 30% missingness; iMODA power decreased with 50% and 30% missingness; MOFA imputation had lower MSE than feature-wise mean, SoftImpute, and KNN across ~10–80% missingness; DeepMF identified simulated MCAR sample/feature subgroups with perfect accuracy at 10%, 50%, and 70% missingness; SUMO slightly outperformed NEMO except at extreme missingness (80% and greater).
- BIDIFAC+ enables bi-dimensional integration across omics and sample cohorts, but took nearly 24 h to converge on a combined TCGA dataset, and assumes shared structures are present across integrated matrices.
Methods
- Narrative review of AI/ML and statistical-learning approaches for multi-omics integration that accommodate missing data, plus background on traditional missing-data mechanisms (MCAR, MAR, MNAR) and imputation/complete-case workflows.
- Methods are categorized by integration strategy (early/concatenation, middle/transformation-based, late/model-based) and by missing-data handling strategy (joint-imputation vs. optimization-masking).
- Table 1 summarizes each reviewed method’s integration approach, missing-data approach, compatible omics platforms, primary use case, application dataset characteristics, and software availability.
Limitations
- The paper states that many reviewed methods implicitly require relatively large sample sizes, as their application datasets contain hundreds to thousands of observations, which may be prohibitive for rare diseases or small studies.
- The paper states that many methods were motivated by or evaluated on sequencing-based omics, with few considering metabolomics, proteomics, or lipidomics, where mass-spectrometry-specific batch effects, missingness mechanisms, and identification uncertainty may violate model assumptions.
- The paper notes that only FBM and iMODA describe how correlation structure is modeled or affects results among joint-imputation approaches, and that many middle-integration methods are black-box with limited interpretability; evident from the text, this is a review rather than an independent benchmark, so performance claims are drawn from original publications and simulations.
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Claims the paper makes
54 Not checked yet · 36 Own interpretation · 14 No basis given
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In the paper
“While all these methods have been presented within the context of multi-omics integration, few explicitly require the use multi-omics data.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
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In the paper
“many of the middle integration methods discussed here are black-box in nature, as the specific relationships learned by each are often incommunicable.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
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In the paper
“However, data integration is one of the primary barriers to effective multi-omics research and reaching the desired holistic view of the biological system being studied (Misra et al., 2019).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Introduction
What the sources say
- Not checked yetcites Misra et al., 2019 source
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In the paper
“Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Machine learning (ML) and artificial intelligence (AI) have shown promise in overcoming some of these hindrances (e.g., Mirza et al., 2019; Zhu, 2020) and have gained popularity in recent years.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Introduction
What the sources say
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In the paper
“However, the observed decreases in predictive performance were observed to be mitigated as the sample size was increased.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Integrative approaches for partially observed multi-omics > Early integration
What the sources say
- Not checked yetcites Fang et al., 2018 source
plaindirect- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: The FBM model assumes gene-gene and methylation-methylation independence, and imputed values reflect this assumption.
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In the paper
“For example, the human proteome project reports that an estimated ~20% of genes yield protein products that are not detected by mass spectrometry, an analytic platform for protein quantification (Paik et al., 2012; Baker et al., 2017).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Introduction
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In the paper
“A similar trend is observed for block-level missingness, which is the type of missingness where several omics (columns) are missing across a set of samples (rows).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Integrative approaches for partially observed multi-omics > Middle integration
What the sources say
- Not checked yetcites Lock et al., 2022 source
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In the paper
“It is not uncommon to have 20–50% of the possible peptide values that are not quantified (Webb-Robertson et al., 2015; Brenes et al., 2019).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Missing data and imputation
What the sources say
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- It may state two separate claims as one. 41
- Probably not a substantial claim. 30
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In the paper
“Identifying these groupings within latent factors is often a post-hoc process, however, and such domain-relevant groupings are not always identifiable within generated latent factors.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
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In the paper
“A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Abstract
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In the paper
“Hinderances to effective integration methods include, but are not limited to, the heterogeneous nature of data and distributional properties across multi-omics datasets, the typically large difference in the number of measured biomolecules compared to number of samples or replicates, and the complex and often noisy nature of biological data.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Introduction
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more ‘omic techologies.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“Furthermore, additional complexities including protein isolation and solubilization, sequence ambiguity, and a lack of standards in statistical thresholds and algorithms result in inconsistent detection of large proportions of protein products that are detected, thus limiting the reproducibility of data collection and analysis (Goh and Wong, 2017).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Introduction
What the sources say
- Not checked yetcites Goh and Wong, 2017
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“In ‘omics studies, data are more commonly MNAR or MAR, considering that instruments are known to have certain detection thresholds (MNAR) and that biochemical dependencies exist between different biomolecules (MAR).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Missing data and imputation
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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.
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In the paper
“If an entire view is missing for one or more samples and must be imputed, any level of similarity specific to that view may be lost.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Multi-omics integration
hedgedconditional- Asserted: passes — The authors put it forward as true.
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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
“Missing data are common in ‘omics data and can arise due to a variety of reasons, such as poor tissue quality, insufficient sample volume, measurement system limitations, budget restrictions, or subject dropout.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Missing data and imputation
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Probably not a substantial claim. 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
“When faced with missing data, these methods thus require a complete case analysis or the implementation of separate imputation steps beforehand.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Multi-omics integration
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“However the authors note that models incorporating gene-gene and methylation-methylation correlations were additionally considered, but these models were associated with a higher computational cost without indicating substantive gains in performance relative to models based on the independence assumption.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Integrative approaches for partially observed multi-omics > Early integration
What the sources say
- Not checked yetcites Fang et al., 2018 source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- 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 authors state this as an assumption: The FBM model assumes gene-gene and methylation-methylation independence, and imputed values reflect this assumption.
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
“Given the prevalence of missing data across different ‘omics, the approach that ML and AI models use in handling missing observations is of great importance when considering multi-omics integration methods.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Introduction
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In the paper
“KNN imputation has been shown to work well for numerical imputation on single datasets (Jadhav et al., 2019), but the algorithm is sensitive to the choice of the number of nearest neighbors (k) and is limited in that it does not utilize the potential structure across the entire set of observations.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Missing data and imputation
What the sources say
- Not checked yetcites Jadhav et al., 2019 source
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In the paper
“While more robust than a complete case analysis since less data are dropped, an available case analysis is still only valid under the MCAR assumption and generates conclusions based on different parts of the data, potentially leading to biased or conflicting inference.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Missing data and imputation
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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In the paper
“However, like mean imputation, imputed values from these approaches may distort feature distributions and lead to biased parameter and variability estimates (Lubin et al., 2004; Succop et al., 2004; Webb-Robertson et al., 2015; Lazar et al., 2016; Bramer et al., 2020).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Missing data and imputation
What the sources say
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Middle integration applies some transformation to represent a complex combination of the datasets before applying downstream analyses.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Multi-omics integration
What the sources say
- Not checked yetcites Ritchie et al., 2015 source
plain- Asserted: passes — The authors put it forward as true.
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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
“Missing samples in multi-omics data are rarely MCAR, and thus inference based on a complete case analysis is likely to be biased.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Multi-omics integration
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Both these methods require complete input, and initial imputation is performed in both cases to accommodate.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Multi-omics integration
What the sources say
- Not checked yetcites Xie et al., 2019 source
- Not checked yetcites Xu et al., 2019
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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
“These methods rely on an imputation pre-processing step, which could possibly negatively affect the downstream analyses, as the relationships between nodes/samples/biomarkers may be biased toward similarities in imputation.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Multi-omics integration
hedgedconditional- Asserted: passes — The authors put it forward as true.
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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
“Integration techniques generally assume measurements from the same samples on several markers, or features, from several ‘omics-data types, which are more generally referred to as data “views”.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Multi-omics integration
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“While this simple approach preserves feature means and grants access to the use of more data, it does not preserve relationships among variables and may lead to an underestimate of variability (Gelman and Hill, 2006).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Missing data and imputation
What the sources say
- Not checked yetcites Gelman and Hill, 2006 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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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
“In metabolomics, limited coverage of the known metabolome increases the risk of overlooking the metabolomic response of interest in targeted metabolomic analyses.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“Early integration is primarily characterized by an initial concatenation of the features across all measured ‘omics, followed by application of methods appropriate for analyzing the resulting high-dimensional dataset.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Multi-omics integration
What the sources say
- Not checked yetcites Ritchie et al., 2015 source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Late integration analyzes each dataset separately and introduces a model or algorithm that combines the outputs of each individual analysis.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Multi-omics integration
What the sources say
- Not checked yetcites Ritchie et al., 2015 source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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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
“Peptides may be missing from one or more samples due to a variety of underlying mechanisms (Daly et al., 2008; Karpievitch et al., 2009), which can result in non-trivial amounts of potential observations being missing.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Missing data and imputation
What the sources say
hedgedconditional- 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.
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
“Given the ubiquity of partially observed cases in multi-omics datasets—and that most integrative multi-omics methods are not inherently compatible with partially observed data—imputation methods are often incorporated as a data pre-processing step within the multi-omics analytic pipeline, or alternatively, partially observed samples are dropped and a complete case analysis is performed.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Missing data and imputation
plaindirect- Asserted: passes — The authors put it forward as true.
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- 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. 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
“Given the tendency of neural networks to overfit to the training data, it is possible that any bias in the imputation of the training data—which is likely due to the common violation of MAR in ‘omics data—will be erroneously exploited by the network.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Multi-omics integration
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The missing percentages considered ranged between ~10–~80%, and all methods demonstrated increases to mean squared error of prediction with increased missing percentage.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Integrative approaches for partially observed multi-omics > Middle integration
What the sources say
- Not checked yetcites Argelaguet et al., 2018 source
boosteddirect- Asserted: passes — The authors put it forward as true.
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- 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
“This approach, while convenient, results in a decrease in sample size, throws away useful data, is only valid under the MCAR assumption and can otherwise lead to biased estimates (Fang et al., 2018; Hawinkel et al., 2020).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Missing data and imputation
What the sources say
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.
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
“Each of the previously described imputation methods have generally been developed within the context of a single dataset, however multi-omics data are uniquely characterized by several separate, yet related, datasets. This aspect of multi-omics has therefore motivated the development of several newer approaches that leverage information across different ‘omics datasets to inform imputations.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Background > Missing data and imputation
plaindirect- Asserted: passes — The authors put it forward as true.
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- 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.
In the paper
“The power of the iMODA framework to detect the effects of interest has been reported to vary according to the proportion of data that are missing. The authors considered cases where 50% and 30% of data were missing, finding that power decreases with increased missingness.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Integrative approaches for partially observed multi-omics > Early integration
What the sources say
- Not checked yetcites Lin et al., 2020 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.
- Rests on an assumption: The known causal hierarchy across biomolecules is that genotype affects omic expression, which influences phenotype.
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
“Related to this point, many of the methods described here were motivated by or evaluated exclusively on sequencing-based omics platforms (e.g., transcriptomics, genomics, and epigenomics) and few considered the fuller breadth of omics platforms that includes metabolomics, proteomics, lipidomics, etc.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
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.
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
“Contrasting with the sequencing-based ‘omics used by many of the methods reviewed in this work, data of other ‘omics are typically generated by a mass spectrometer, an instrument whose use introduces several nuances to the data which may mean integration model assumptions reasonably made for sequencing-based data may not hold for mass spectrometry-based data.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
hedgedconditional- 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.
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
“The datasets considered by these approaches contain hundreds, if not thousands, of observations, sample sizes that may be practically prohibitive due to cost, biospecimen availability, or both.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
hedged- 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.
In the paper
“Notably, there is no constraint on the number of detected modules, nor is there an upper bound on module size.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Integrative approaches for partially observed multi-omics > Late integration
What the sources say
- Not checked yetcites Rappoport 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.
In the paper
“However, Lock et al. (2022) note that BIDIFAC+ assumes that shared structures are present across the integrated matrices, an assumption that may be violated in practice.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
What the sources say
- Not checked yetcites Lock et al., 2022 source
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: 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 authors state this as an assumption: BIDIFAC+ assumes that shared structures are present across the integrated matrices.
In the paper
“This PoE model architecture is robust to partially observed samples, combining only the ‘omic-specific encodings of each partial sample's observed ‘omics during model training.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Integrative approaches for partially observed multi-omics > Middle integration
What the sources say
- Not checked yetcites Wu and Goodman (2018)
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.
- The authors state this as an assumption: MVAE assumes that data views are independent from one another, given a shared latent representation.
None of the sources cited for this claim have been checked yet.
In the paper
“As well, the range of adjusted rand index values (the performance metric used) was typically observed to be narrower for SUMO, suggesting less variability in expected performance.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Integrative approaches for partially observed multi-omics > Middle integration
What the sources say
hedgedcorrelational- 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
“The authors circumvent this issue by proposing a heuristic algorithm that identifies a local maximum and recommend using the best solution from repeated runs of the algorithm to better approximate the globally maximal solution.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Integrative approaches for partially observed multi-omics > Late integration
What the sources say
- Not checked yetcites Rappoport et al., 2020 source
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.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“However, it should be noted that these evaluations were completed in a simulated data setting in which data were simulated as missing completely at random (MCAR) and ground truth feature subgroups were generated with simple discrimination boundaries.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Integrative approaches for partially observed multi-omics > Middle integration
What the sources say
- Not checked yetcites Chen et al., 2019 source
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
“These methods also commonly investigate differences in “early”, “middle”, and “late” fusion and work has even been done to address the view-missing and view-heterogeneity problems (Nagrani et al., 2018; Wang et al., 2020).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
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. 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
“The criteria of sets of features from distinct views across a shared set of samples is applicable to any domain where the combination of different data sources provides a more complete representation of the system under investigation.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
plain- 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.
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
“On the other hand, though dependencies exist between different ‘omics data, important differences between ‘omics must also be accounted for in developed integrative approaches.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
plaincorrelational- 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 untargeted metabolomics and lipidomics false discovery rates are difficult to compute and largely not accurate (Stein, 1994; Jeong et al., 2011; Matsuda et al., 2013; Kim and Zhang, 2014).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
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.
In the paper
“This algorithm incorporates partially observed samples by representing their missing components as edgeless nodes within their corresponding edge-weighted graphs.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Integrative approaches for partially observed multi-omics > Late integration
What the sources say
- Not checked yetcites Rappoport et al., 2020 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
“These cases present the need for methods that generate reliable inference despite smaller sample sizes and avoid overfitting to the data.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
plaindirectn = 16- 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
“many of the deep-learning architectures mentioned in this review have similarities to ones used in analysis of digital media that try to combine multiple modalities to improve predictive performance of video content (Carreira and Zisserman, 2017; Afouras et al., 2019).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
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
“For example, a study to identify biomarkers for Snyder Robinson syndrome, a rare neurological disorder, obtained data on 16 patients, only three of which presented with the disorder of interest (Abela et al., 2015).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
What the sources say
- Not checked yetcites Abela 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.
None of the sources cited for this claim have been checked yet.
In the paper
“manual verification of metabolites following identification scoring is required for shotgun metabolomics, as scoring methods are much less mature than for other ‘omics data types (Stein and Scott, 1994; Samaraweera et al., 2018).”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
What the sources say
- Not checked yetcites Stein and Scott, 1994
- Not checked yetcites Samaraweera et al., 2018 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.
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
“We describe this requirement as “implicit” given that it is rarely, if ever, stated as a limitation, but instead implied based on the sample sizes of the datasets that these methods were successfully applied to.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
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
“Using the area under the receiver-operator curve (AUROC) as a performance metric, the authors found that the performance of DeepIMV diminished with increased missingness but performed as well as or better than the compared approaches.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Integrative approaches for partially observed multi-omics > Middle integration
What the sources say
- Not checked yetcites Lee and van der Schaar, 2021
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.
In the paper
“As well, they noted that BIDIFAC+ took nearly 24 h to reach convergence when fitted to a combined TCGA dataset, and thus the method may not scale well with larger aggregations of datasets.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
What the sources say
- Not checked yetcites Lock et al., 2022 source
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: 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
“Kok et al. (2018) notes, for example, that one may need around 19 samples per group to detect a fold change of 1.5 in microRNA data at 80% power and false discovery rate <10%.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
What the sources say
- Not checked yetcites Kok et al., 2018 source
hedgedn = 19- 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
“Insights from the ‘omics domain, where some of these issues are most salient, could transfer to other domains.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
hedgedconditional- 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: passes — The paper gives a 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.
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
“The methodological choices inherent to the ‘omics domain are usually the ones that offer the ability to constrain or elucidate relationships between biomarkers.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
plaindirect- 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.
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
“the groupings implicit within the latent factors generated by these models may be contextualized according to domain-specific knowledge (e.g., shared molecular pathways), thus increasing their interpretability and allowing one to infer underlying relationships.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
hedgedconditional- 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.
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
“Some common choices are the use of principal-component-like representations that might identify important groups of biomarkers, and the injection of prior knowledge through techniques that constrain the relationships between features such as directed edges in graph representations, hard parameter constraints, or carefully chosen priors in”
- Also stated in: § Discussion · plain
“the relationships between features such as directed edges in graph representations, hard parameter constraints, or carefully chosen priors in Bayesian frameworks.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
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.
- Also stated in: § Discussion · plain
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
“incorporating domain knowledge may be key toward bridging the interpretability gap apparent across these methods.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
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.
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
“If the generation of these latent factors were instead guided by domain knowledge, resulting groupings may be more likely to hold relevance and rely less on post-hoc analyses for contextualization”
- Also stated in: § Discussion · hedged
“instead guided by domain knowledge, resulting groupings may be more likely to hold relevance and rely less on post-hoc analyses for contextualization within the scientific domain.”
§ Missing data in multi-omics integration: Recent advances through artificial intelligence > Discussion
hedgedconditional- 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: passes — The paper gives a 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.
- Also stated in: § Discussion · hedged
The paper relies on this without support.
Existing multi-omics data integration review articles either do not address or only briefly mention methods for handling missing observations.
“Despite the importance of considering methods for handling missing observations, this topic is either not addressed or is only briefly mentioned in existing multi-omics data integration review articles.”
The authors state this as an assumption.
The iBAG approach assumes completely observed data.
“The iBAG approach assumes completely observed data, and to remedy this, the FBM approach introduces a third, imputation layer that models missing gene expression and methylation data through multivariate normal distributions.”
The authors state this as an assumption.
The FBM model assumes gene-gene and methylation-methylation independence, and imputed values reflect this assumption.
“Also worth noting is that the FBM model assumes gene-gene and methylation-methylation independence and imputed values reflect this assumption accordingly.”
The authors state this as an assumption.
MVAE assumes that data views are independent from one another, given a shared latent representation.
“One key assumption made by this approach is that data views are independent from one another, given a shared latent representation.”
The authors state this as an assumption.
BIDIFAC+ assumes that shared structures are present across the integrated matrices.
“Lock et al. (2022) note that BIDIFAC+ assumes that shared structures are present across the integrated matrices, an assumption that may be violated in practice.”
The paper relies on this without support.
The known causal hierarchy across biomolecules is that genotype affects omic expression, which influences phenotype.
“The proposed framework is aligned with the known causal hierarchy across these biomolecules, i.e., genotype affects ‘omic expression which influences phenotype.”
The paper relies on this without support.
Gene expression is known to be affected by changes to the DNA sequence.
“For example, gene expression is known to be affected by changes to the DNA sequence.”
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 cited source says what the claim attributes to it.
In the citing paper
“Although these methods are simple and easy to implement, they can introduce bias and reduce data variability (Flores et al., 2023).”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Flores et al., 2023
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · cited 2×
“Missing ‘omics data are accommodated through an expectation-maximization (EM) approach (Dempster et al., 1977) for parameter estimation.”
MethodOn Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Auto-Encoding Variational Bayes
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“For example, many of the deep-learning architectures mentioned in this review have similarities to ones used in analysis of digital media that try to combine multiple modalities to improve predictive performance of video content (Carreira and Zisserman, 2017; Afouras et al., 2019).”
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Missing Value Estimation Methods for DNA Microarrays
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- A variational information bottleneck approach to multi-omics data integration,15131521 LeeC. van der SchaarM. International Conference on Artificial Intelligence and Statistics2021
- Sindhwani, 2005, A co-regularization approach to semi-supervised learning with multiple views,
Cited by
Sorted by citations
Items 101–175 of 175
Deep-Learning Uncovers certain CCM Isoforms as Transcription Factors.
Croft, J., Gao, L.-Y., Sheng, V., & Zhang, J. (2024) · Frontiers in Bioscience · doi:10.31083/j.fbl2902075 · Cited by 2
“This encompasses the investigation of diverse elements involved in gene expression regulation [25], such as transcription factor binding sites [26–28], transcription start sites (TSSs) [28], missing data extrapolations [29], and other related genomic components [25,30].”
BackgroundSystematic benchmarking of omics computational tools
Kumar, S., Singh, M., Sharma, R. K., & Gupta, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-16092-9.00004-7 · Cited by 1
Distilling Mechanistic Models From Multi-Omics DataPreprint
Erwin, S., Fletcher, J. R., Sweeney, D. C., Theriot, C. M., & Lanzas, C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.06.556597 · Cited by 2
Fehr, K. B., Mertens, A., Shu, C.-H., Dailey-Chwalibóg, T., Shenhav, L., Allen, L. H., Beggs, M. R., Bode, L., Chooniedass, R., DeBoer, M. D., et al. (2025) · doi:10.12688/verixiv.402.1 · Cited by 2
Liharska, L. E., & Charney, A. W. (2024) · Current topics in behavioral neurosciences · doi:10.1007/7854_2024_466 · Cited by 1
A Two-Step Algorithm for Handling Block-Wise Missing Data in Multi-Omics
Baena‐Miret, S., Reverter, F., Sánchez‐Pla, Á., & Vegas, E. (2025) · Applied Sciences · doi:10.3390/app15073650 · Cited by 2
Metabolic Modeling and Flux Analysis: Intersection with Other Omics Techniques
Sushmitha, J., Kumar, C. J. T., Hrishikeshan, K. N., Singh, T., Kavya, T. S., & Vinutha, T. (2025) · Springer eBooks · doi:10.1007/978-981-96-4824-5_5 · Cited by 2
Sinha, K., & Srivastava, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21793-7.00023-4 · Cited by 2
MACHINE LEARNING APPROACHES FOR INTEGRATIVE MULTI-OMICS DATA ANALYSIS OF CANCER: A SYSTEMATIC REVIEW
Hassan, A. M., Naeem, S. M., Eldosoky, M. A. A., & Mabrouk, M. S. (2025) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237225300044 · Cited by 2
Artificial intelligence-based epitope discovery of Mpox virus: Rational vaccine design
Adugna, A., Abebaw, D., Admasu, A., et al. (2025) · Vaccine X · doi:10.1016/j.jvacx.2025.100686 · Cited by 1
Alves, E., Gurupadayya, B., & Prabhakaran, P. (2025) · Current Analytical Chemistry · doi:10.2174/0115734110401212250806053745 · Cited by 1
Singh, D. P., Maurya, S., Prabha, R., Satnami, L., & Rai, N. (2025) · Springer eBooks · doi:10.1007/978-981-95-2294-1_8 · Cited by 1
Chen, H., Tang, R., Hong, M., Zhao, J., Lu, D., Luan, X., Zheng, G., & Zhang, W. (2025) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(25)60903-4 · Cited by 2
Dimensionality-modulated generative AI for safe biomedical dataset augmentation
Lötsch, J., Himmelspach, A., & Kringel, D. (2025) · iScience · doi:10.1016/j.isci.2025.114321 · Cited by 1
Keenan, L., Younger, R. M., Race, P. R., & Bawn, M. (2025) · Emerging Topics in Life Sciences · doi:10.1042/etls20253019 · Cited by 2
A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities
Zanitti, M., Miskovic, V., Trovò, F., Pedrocchi, A., Shen, M., Tun, Y. K., Prelaj, A., & Kosta, S. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401431 · Cited by 1
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Human Systems Immunology in the Omics Era: Challenges, Methods, and Emerging Directions
Riemann, L., & Förster, R. (2026) · European Journal of Immunology · doi:10.1002/eji.70164 · Cited by 1
“In high-dimensional systems immunology datasets, specialized approaches such as matrix factorization can often effectively deal with missing data [ 66, 67 ].”
Abhishek, L., Shravya, B. U., Kiran, N. S., Haldar, A., Dakal, T. C., & Rajagopal, S. (2026) · Journal of Plant Biochemistry and Biotechnology · doi:10.1007/s13562-026-01064-0 · Cited by 1
“…machine learning models—including Random Forests, XGBoost, and attention-based neural net-works—are trained with biologically constrained hyper-parameters and explained using SHAP or attention-weight extraction to ensure agronomic relevance and translational utility (Flores et al. 2023 ).”
Safety assessment of emerging food technologies: Novel and genome-edited foods
Ham, J.-H., Lee, Y.-J., & Kim, H. (2026) · Future Foods · doi:10.1016/j.fufo.2026.101093 · Cited by 1
Jiang, X.-Z., Guo, L.-J., Zhang, M.-T., Ji, G.-L., Liu, H.-T., Zheng, Y., & Zhou, J. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1846105 · Cited by 2
“Clinical records are often collected irregularly and vary in completeness across patients, whereas omics data typically consist of thousands of molecular features measured simultaneously (82, 83).”
Integrative Bioinformatics Approaches in Environmental Biotechnology: A Review
Teklay, Y. T. (2026) · The Scientific World JOURNAL · doi:10.1155/tswj/3495506 · Cited by 1
“Methodologically, the integration of multiomics layers is computationally demanding and often lacks a unified statistical framework to weigh the relative importance of different data types, such as transcript abundance versus protein expression [64].”
Li, N., Yang, F., Zhang, Q., et al. (2024) · IEEE International Conference on Medical Artificial Intelligence (MedAI) · doi:10.1109/medai62885.2024.00088 · Cited by 1
“To address this challenge, various methods have been developed, such as multi-kernel learning, Bayesian consensus clustering, machine learning (ML)-based dimensionality reduction, similarity network fusion, and deep learning (DL) methods [7], [8].”
Microbial Ecotoxicology—40 Years on
Ford, T. (2025) · Life · doi:10.3390/life15040514 · Cited by 1
“While these latter problems may eventually be addressed through bioinformatics and AI, routine application would be very much for the future [81].”
Gintegrator: Enhancing biological sequences data integration with real-time identifier translation
Sola, F., Ayala, D., Pulido, M. R., Ayala, R., López‐Cerero, L., Hernández, I., & Ruiz, D. (2025) · SoftwareX · doi:10.1016/j.softx.2025.102041 · Cited by 1
Ballard, J. L., Dai, Z., Shen, L., Long, Q., & Initiative, A. D. N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.16.659949 · Cited by 1
SynOmics: Integrating Multi-omics Data Through Feature Interaction NetworksPreprint
Alif, M. N., & Zhang, W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.17.676954 · Cited by 1
Applications of AI in the Genomic Analysis of Crop Plants for Improved Agricultural Outcomes
Shah, S., Vora, N., & Shah, M. (2026) · doi:10.1002/9781394330485.ch5 · Cited by 1
Han, S., Yu, S., Shi, M., Harada, M., Ge, J., Lin, J.-S., Prehn, C. P., Petrera, A., Li, Y., Sam, F., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.26.559302
Cosentini, I., Condorelli, D. F., Locicero, G., Ferro, A., Pulvirenti, A., Barresi, V., & Alaimo, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0301591
“On one hand, big data provides an unprecedented opportunity in biology, on the other hand great challenges arise in terms of data mining, data integration and knowledge discovery [2].”
BackgroundIntegrative Analysis of Multi-Omics Data
Yue, Z., & Mohamed‐Hussein, Z. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00233-5
Integrative Bioinformatics Tools for Cancer Research
Settino, M., & Cannataro, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00197-4
Integrating Hidden Features of ELM Auto-Encoder for Cancer Multi-Omics Data Clustering
Duan, X., Hu, C., Yun, M., et al. (2024) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice63571.2024.10864066
“The 5 integration methods include Spectrum[11],SNF[12],CIMLR[13],NEMO[14] and PINSplus[15].”
Multi-omics and drug development
Hamid, S., Bhat, A. A., Bhat, M. R., Assad, A., & Macha, M. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13595-8.00007-6
Birrenkott, D. A., & Kabrhel, C. (2025) · Thrombosis and Haemostasis · doi:10.1055/a-2575-3388
Multimodal Integration in Healthcare: Development with Applications in Disease Management (Preprint)Preprint
Li, K., & Hao, Y. (2025) · doi:10.2196/preprints.76557
Integrating Advanced Metabolomics with Plant Functional Genomics
Shaikh, A., Toppo, V., Al-Khayri, J. M., & Nagella, P. (2025) · CABI biotechnology series. · doi:10.1079/9781800626911.0017
Lötsch, J., & Kringel, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5366515
Varol, B., Ömürlü, İ. K., & Türe, M. (2025) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2025.2534610 · Cited by 1
“These datasets often contain hundreds or thousands of variables – such as gene expression levels and proteomic features – relative to a smaller number of samples [1,2].”
Evaluating Multiomics Integration Architectures for Training With Structured MissingnessPreprint
Fisher, S., Bradley, J., Lansdown, G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.10.672554
“There exist well established methods for handling feature-level missingness, including joint imputation and masking (5, 6), however these do not always transfer well to modality-wise structured missingness (12, 13).”
Gradient Descent to Predict Enzyme Inhibition
da Silva, A. D., & de Azevedo, W. F. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4949-7_5
Zhao, R., Li, W., Gonzelez-Aguirre, J., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5600413
Use of artificial intelligence and machine learning for multiomics
Karađuzović-Hadžiabdić, K., & Gruca, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33064-3.00018-9
Neguț, I., Oliveira, C. S. F., & Vișan, A. I. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5258-9.ch001
Baba, M., & Mohideen, H. S. (2026) · Advances in biochemistry in health and disease · doi:10.1007/978-3-032-08824-6_7
A fast integrative clustering and feature selection approach for high-dimensional multiview data
Alnajjar, A., Bian, H., & Lu, Z. (2026) · Statistical Methods in Medical Research · doi:10.1177/09622802251406584
PrivCervBoost: Privacy-Enhanced Federated Gradient Boosting for Cervical Cancer Risk Prediction
Meenakshisundaram, N., & Sajiv, G. (2025) · doi:10.1109/iciteics64870.2025.11341361
Bioinformatics Tools and Data Integration in Aquaculture Omics to Enhance Food Security
Kumar, G., Brahmchari, R. K., Kumar, R., & Srivastava, P. P. (2026) · Springer eBooks · doi:10.1007/978-3-032-14971-8_14
Context‐Specific Genome‐Scale Metabolic Modeling to Unravel the Metabolic Rewiring in Human Diseases
Beura, S., Bhadoriya, D. R. S., Dash, S., Das, A. K., & Ghosh, A. (2026) · Biotechnology Journal · doi:10.1002/biot.70213
Abdelaziz, E. H., Amin, E., Ismail, R. M., & Mabrouk, M. S. (2026) · BioData Mining · doi:10.1186/s13040-026-00530-8
“A comprehensive experimental setup was designed involving four approaches: [1] Dynamic Available-Case approach [2], Exhaustive Available-Case approach [3], Impu-tation-Based approach, and [4] The Proposed Hybrid Approach.”
Wu, Z., Yu, X., Ren, H., Shangguan, C., Han, C., Yang, X., Qi, S., Duan, X., Wang, H., Hu, P., et al. (2026) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2026.111802
Decoding the Complexity of COPD, Multi – Omics approaches to Diagnosis and Treatment
Bhat, A. A., Hussain, I., Shafi, T., Malik, S., Haq, S. I. U., & Ashraf, R. (2026) · Pan-African Journal of Health and Psychological Sciences · doi:10.64261/s36w6x72
Artificial intelligence in cancer imaging
Richi, M., Khan, M., & Mujahid, M. H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45004-4.00012-8
Computational and multi-omics systems biology for precision microbiome therapeutics
Dewan, A., & Mascellino, M. T. (2026) · Frontiers in Microbiomes · doi:10.3389/frmbi.2026.1842701
“This imbalance can introduce biases in integrative analyses, reduce statistical power, and constrain model interpretability (Flores et al., 2023).”
Shi, H., Zhang, C., Zhao, J., Zhai, Q., Chen, W., & Tian, F. (2026) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2026.105835
Velegzhaninov, I. O., Kazakov, D., Rasova, E. E., Zaplavnaia, S., & Klokov, D. (2026) · ACS Synthetic Biology · doi:10.1021/acssynbio.5c00873
Modern modeling approaches for data gap filling
Borrel, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36474-7.00004-1
Gupta, A., & Chawla, S. (2026) · OMICS A Journal of Integrative Biology · doi:10.1177/15578100261457662
“Likewise, zero-in fl ation and missing data is also a limitation of OMICS data-sets as it involves missing features detection due to technical limitation; even high coverage techniques such as LC – MS/ MS are unable to complete the datasets (Flores et al., 2023).”
Plant–Microbe Interactions Studied Using Spatial and Multiple Omics Tools
Dehbi, I., Benamar, K., Janati, M., Zennouhi, H., & Lahlali, R. (2026) · CABI biotechnology series. · doi:10.1079/9781836994572.0010 · Cited by 1
Alsharoh, H., Ismaiel, A., Călin, G. A., Pop, O. L., Berindan‐Neagoe, I., & Bender, A. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag338
“However, this integration approach faces the challenge where all assayed biomolecules need to be shared across all -omics [ 53, 54].”
Abbasi, A. F., Sajjad, M., Vollmer, S., Dengel, A., & Asim, M. N. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.23.740273 · Cited by 1
A heterogeneous multimodal ensemble framework for multi-omics breast cancer prognosis
Bozorgpour, R., & Sadrabadi, M. S. (2026) · Clinical Cancer Bulletin · doi:10.1007/s44272-026-00068-0 · Cited by 1
“Practical challenges such as missing data, batch effects, experimental variability, and differences in sequencing platforms further reduce the robustness and reproducibility of down-stream analyses [19].”
Li, J., Ye, T., Xiao, Y., Zhao, M., Jiang, L., Chen, S., Guo, F., & Tang, J. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag435
“However, multi-omics clustering remains challenging because omics profiles are often high-dimensional, sparse, heterogeneous, and noisy, with substantial variation in data quality across modalities and samples [ 10– 13].”
AI-integrated digital breeding for crop improvement
Son, S., & Park, S. R. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1929693
Systems biology and multi-omics integration
Hossein‐Zadeh, N. G. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45663-3.00006-3
Boşgelmez, İ. İ., Mertaş, B., & Deliser, H. (2026) · Frontiers in Toxicology · doi:10.3389/ftox.2026.1871830
“In proteomics and LC-MS workflows, MNAR often occurs when analytes fall below detection limits, and treating these censored values as MAR can bias abundance estimates and downstream inference (Karpievitch et al., 2012; Flores et al., 2023).”
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Kapila, A., Sherin, Z., Bashir, O., Mondal, I. H., Pawase, P. A., Hamid, Vahidi, M., & Abdi, G. (2026) · Applied Food Research · doi:10.1016/j.afres.2026.102533
TMO-Net+: An Enhanced Tumor Multi-Omics Pre-Trained Network for Multi-Task Learning in Oncology
Liu, W., Liu, X., Zhou, S., Li, K., Wang, X., Chen, K., Guo, L., Zhang, R., & Su, Q. (2026) · Genes · doi:10.3390/genes17091085 · Cited by 1
“Due to constraints in tissue quality, sequencing costs, or platform discrepancies, individual patients often lack complete omics profiles, making unpaired observations the norm rather than the exception [16].”
Microbial Metabolites in Agriculture: Promises and Limitations
Tripathi, Y. N., & Raghuwanshi, R. (2026) · The Open Agriculture Journal · doi:10.2174/01187433154883260914045249
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“ML algorithms have been used to manage the problem of missing data in multi-omics datasets (Baião et al. 2025; Flores et al. 2023), integrating unpaired data (Kan et al. 2025), and addressing the challenges of high dimensionality (Zhou et al. 2024) and multimodal datasets (Lin et al. 2024).”
AI-Driven Multiple Omics for Personalized Cancer Medicine
Mani, M., Balamurugan, Y., Sundaram, P. D., Jaffer, S. B. M., Dharumaiyan, N. D., Shanmugam, G., Philomenathan, A. P. J., & Anand, A. V. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9715-3.ch001
Integration of Multi-Omics in AI-Based Models for Predictive Oncology
Nativel, A., Dandou, S., Favier, F., Lesage, C., Mangé, A., Aligon, J., Coopman, P. J., & Larive, R. M. (2026) · Genes · doi:10.3390/genes17101231
Metadata
- Authors
- Flores, J. E., Claborne, D. M., Weller, Z. D., Webb-Robertson, B.-J. M., Waters, K. M., & Bramer, L. M.
- Affiliations
- Pacific Northwest National Laboratory (US)
- Corresponding author
- Bramer, Lisa M.
- Venue
- Frontiers in Artificial Intelligence
- Publisher
- Frontiers Media SA
- Published
- 2023-02-09
- Volume
- 6
- Pages
- 1098308-1098308
- ISSN
- 2624-8212 (ISSN-L)
- PMC
- PMC9949722
- OpenAlex
- W4319759388
- Semantic Scholar
- d833d42089ae664847832f8c0ef7bd7f6c1e26b6
- Cited by
- 181
- References
- 148
- Citations by year
- 2026: 55 · 2025: 77 · 2024: 38 · 2023: 11
- FWCI
- 4.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Artificial Intelligence (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Pacific Northwest National Laboratory
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- missing data, multi-omics integration, artificial intelligence, statistical learning, missing data imputation, data heterogeneity, biological systems
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