Genome Wide

Genome-wide studies aim to understand the complete genetic landscape and its relationship to various biological phenomena, from gene expression and 3D genome organization to drug response and disease. Current research heavily utilizes machine and deep learning, including transformer models and novel algorithms like Portal Learning and CLUB-PLS, to analyze high-dimensional genomic and related data (e.g., transcriptomics, imaging data), predict complex interactions (e.g., enhancer-promoter interactions, protein-ligand binding), and improve the accuracy of genome-wide analyses like sequence alignment. These advancements are significantly impacting fields like drug discovery, by enabling the identification of novel therapeutic targets and the design of more effective interventions, and improving our understanding of complex biological processes.