Deep Learning in Multiomics Sciences: Where We are, Emerging Topics, and Future Challenges

Multiomics is an emerging biological analysis approach in which the datasets come from multiple “omics”, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics. Nowadays, the convergence of Deep Learning and multiomics sciences presents an unprecedented opportunity to dissect the intricate interplay of biological processes. Specifically, multiomics data integration, propelled by Deep Learning methodologies, has revolutionised biological research, enabling a more holistic understanding of complex biological systems and disease mechanisms. This paper explores the current landscape of Deep Learning applications in multiomics, highlighting state-of-the-art techniques, emerging research areas, and the challenges that lie ahead. In particular, we delve into the application areas and computational methods that have been considered so far, offering guidance to researchers navigating this intricate field.