Many cancer mutations occur in regions of the human genome that do not code for proteins. These non-coding regions serve as vital regulators of gene expression; mutations in these regions contribute to various hallmarks of cancer. Elucidating these regulatory elements and their malignant variants is critical for advancing our understanding of cancer biology and fostering precision medicine. Deep learning sequence models can substantially enhance our grasp of the regulatory genome in both health and disease. To this end, Dr. Wang aims to combine generative AI models with single-molecule regulatory genomics to uncover the principles that underlie the cancer regulatory genome at unprecedented resolution and precision.
With single-molecule regulatory genomics, Dr. Wang will develop a deep generative AI model to learn the probability landscape of the single-molecule regulatory genome. By taking any DNA sequence as input, the deep generative AI model can generate diverse configurations of single-molecule chromatin states.
