Visualization, Statistical Modeling and Discovery in Computational Epigenomics

Epigenetics is recognized as one of the most important emergent fields in Computational Biology. The study of epigenetic mechanisms in development and disease using high-throughput techniques has been one of the most active areas in life and clinical sciences in the last five years. In this talk, I will present advances in statistical learning methods and data visualization for computational epigenomics and the fundamental discoveries of molecular mechanisms in cancer facilitated by these tools. Along the way, I will describe novel methods for (a) detecting genomic regions of significant epigenomic modification in cancer based on data smoothing methods, (b) learning of genomic predictive signatures based on modeling hyper-variability, and (c) systems and tools for effective computational and visual interactive exploratory data analysis.