Integration strategies of multi-omics data for machine learning analysis
| Autor: | Olivier Perin, Marie-Pier Scott-Boyer, Milan Picard, Arnaud Droit, Antoine Bodein |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Biophysics Genomics Network Review Article computer.software_genre Machine learning Biochemistry Omics data 03 medical and health sciences 0302 clinical medicine Structural Biology Genetics 030304 developmental biology 0303 health sciences Multi-omics business.industry Deep learning Multi-view Omics Computer Science Applications 030220 oncology & carcinogenesis Multi omics Artificial intelligence business computer TP248.13-248.65 Data integration Integration strategy Biotechnology |
| Zdroj: | Computational and Structural Biotechnology Journal, Vol 19, Iss, Pp 3735-3746 (2021) Computational and Structural Biotechnology Journal |
| ISSN: | 2001-0370 |
| Popis: | Graphical abstract Schematic representation of the main strategies for multi-omics datasets integration. A) Early integration concatenates all omics datasets into a single matrix on which machine learning model can be applied. B) Mixed integration first independently transforms or maps each omics block into a new representation before combining them for downstream analysis. C) Intermediate integration simultaneously transforms the original datasets into common and omics-specific representations. D) Late integration analyses each omics separately and combines their final predictions. E) Hierarchical integration bases the integration of datasets on prior regulatory relationships between omics layers. Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems. Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications. |
| Databáze: | OpenAIRE |
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