Scoring 3D protein-protein interaction models using deep learning
Cancer affects millions of people worldwide. With the advent of novel DNA sequencing technologies, genome sequencing has now started to become part of a routine workflow for cancer diagnostics and potentially enables fine-tuned treatment strategies tailored towards individual cancer patients. In spite of the massive genomic data production, systematic and comprehensive analysis of these data, in particular regarding the detection and interpretation of structural variation, are lagging behind due to computational and algorithmic limitations.
In this project, we will create novel analytical and computational frameworks that lead to fast, cost-efficient and comprehensive detection and annotation of structural variations in cancer genomes. We particularly focus on previously neglected variations occurring in unexplored regions of the cancer genome. Our methods will serve as an important component in future genome-first-based clinical-decision making for cancer patients and is essential to drive discovery of novel cancer genes and mechanism from modern-day whole genome sequencing data.