A Review of Deep Learning Methods for Multi-omics Integration in Precision Medicine

Omics integration in the field of bioinformatics and computational biology has advanced pharmaceutical research and ultimately precision medicine. Recently, biomedical research has grown exponentially in terms of the gathering of genetic and molecular profiles of humans, posing great interest in linking omics with phenotypic data. The availability of different omics (e.g., genomics, transcriptomics, epigenomics, glycomics, proteomics, metabolomics, and lipidomics) has created opportunities in precision medicine, by linking an individual’s unique omics profile and phenotypic data such that the relationship between genotype and phenotype, disease diagnosis, disease subtyping, and disease prediction can be understood. There are tremendous challenges in merging, analyzing, and interpreting the biological insights of these omics datasets that are growing exponentially. In this paper, we review the most recent advancements in machine learning and deep learning-based approaches to harmonize multiomics and phenotype data. Finally, we discuss the various challenges in integrating omics with phenotypic data and future directions.