Journal article
Missing data in multi-omics integration: Recent advances through artificial intelligence
Frontiers in artificial intelligence, Vol.6, p.1098308
02/09/2023
DOI: 10.3389/frai.2023.1098308
PMCID: PMC9949722
PMID: 36844425
Abstract
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.
Details
- Title: Subtitle
- Missing data in multi-omics integration: Recent advances through artificial intelligence
- Creators
- Javier E. Flores - Pacific Northwest National LaboratoryDaniel M. Claborne - Pacific Northwest National LaboratoryZachary D. Weller - Pacific Northwest National LaboratoryBobbie-Jo M. Webb-Robertson - , , , ,Katrina M. Waters - Pacific Northwest National LaboratoryLisa M. Bramer - Pacific Northwest National Laboratory
- Resource Type
- Journal article
- Publication Details
- Frontiers in artificial intelligence, Vol.6, p.1098308
- DOI
- 10.3389/frai.2023.1098308
- PMID
- 36844425
- PMCID
- PMC9949722
- NLM abbreviation
- Front Artif Intell
- ISSN
- 2624-8212
- eISSN
- 2624-8212
- Publisher
- Frontiers Media S.A
- Grant note
- ;
- Language
- English
- Date published
- 02/09/2023
- Academic Unit
- Biostatistics
- Record Identifier
- 9985112968502771
Metrics
2 Record Views