We aim to identify the associations between key components on genome and epigenome levels and understand how their perturbation can lead to cancer. In particular, we explore DNA mutational patterns, their dependence on DNA local sequence context, nucleosome structural and dynamic features with an ultimate goal to identify underlying mutagenic processes and decipher cancer driving events.<b> </b>To accomplish this, we develop and apply machine learning, molecular modeling, molecular dynamics simulation methods and use experimental data to guide us in designing hybrid approaches. Such methods provide a predictive power and offer experimental leads.

Decoding molecular mechanisms of cancer mutations with computational and hybrid approaches

ACS Spring Meeting 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.