Recent technological developments have led to an explosion of data including gene expression and genome architecture in single cells and within tissues. A major experimental and computational challenge going forward is how to integrate all the emerging imaging and sequencing data to identify regulatory modules in health and disease.
The rapid progress in machine learning and AI yields exciting opportunities for combining and exploiting such diverse genomic datasets.
This conference will bring together leading international experts, early-career scientists and students at the intersection of genome biology and machine learning to spur new collaborations across disciplinary boundaries. This is critical for uncovering the mechanogenomic codes that link genome architecture, regulation and function, and ultimately for the development of new biomarkers and therapeutic interventions.