Integration strategies of multi-omics data for machine learning analysis
By Milan Picard, Marie-Pier Scott-Boyer, Antoine Bodein, Olivier Périn, Arnaud Droit. Computational and Structural Biotechnology Journal, 2021
525 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/380da66b8f7b1312f53322961613aa9475464459
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In Molecular Biology
Citations in the corpus
- Multimodal data fusion for cancer biomarker discovery with deep learning (2023, background): “The increasing collection of large cohorts of multi-omics cancer data has spurred several efforts to fuse multi-omics data to fully grasp the tumour profile and several models for survival and risk prediction have been proposed 4,6,56,85–93 .”