Integration strategies of multi-omics data for machine learning analysis

Abstract

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.

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods

“…In recent years, the combination of high throughput phenotyping and genotyping tools has greatly accelerated the plant breeding progress ( Yang et al, 2020 ). However, merging different omics datasets for better characterization of the complex traits extensively depends on an appropriate selection of a data integration strategy ( Picard et al, 2021 ). Extracting the biological information from the secondary related traits that are in high correlation with the trait of interest, detecting the associated SNPs, and identifying candidate genes for each detected peak SNPs provided a valuable complement for understanding the biological mechanism of a trait of interest.…”

“…In addition to the proper ML algorithm choice, adopting an accurate data integration strategy is required for a better understanding of the structure of complex multidimensional traits at different omics levels ( Tarazona et al, 2021 ). These days, more and more data are generated using different omics such as genomics and phenomics, and several data integration strategies such as early, intermediate, late, mixed, and hierarchical strategies are available ( Jamil et al, 2020 ; Picard et al, 2021 ). A hierarchical data integration strategy is built upon prior knowledge about the relationship between and among different tested omics layers ( Picard et al, 2021 ).…”

“…These days, more and more data are generated using different omics such as genomics and phenomics, and several data integration strategies such as early, intermediate, late, mixed, and hierarchical strategies are available ( Jamil et al, 2020 ; Picard et al, 2021 ). A hierarchical data integration strategy is built upon prior knowledge about the relationship between and among different tested omics layers ( Picard et al, 2021 ). For instance, a hierarchical data integration strategy can be used for a better understanding of soybean yield formation by having prior knowledge about the physiological concept of each hyperspectral reflectance in explaining the overall yield variation.…”

The Role of Omics Approaches to Characterize Molecular Mechanisms of Rare Ovarian Cancers: Recent Advances and Future Perspectives

“…Multi-omics data integration can also help in deciphering non-linear processes in addition to linear processes. In addition, multi-omics data integration is a prerequisite step for analyzing omics datasets using machine learning algorithms [81]. Several computational approaches exist that can help integrate multiple omics datasets.…”

“…multi-omics data integration is a prerequisite step for analyzing omics datasets using machine learning algorithms [81]. Several computational approaches exist that can help integrate multiple omics datasets.…”