Genomic Data Problems and Algorithm Solutions: Vignettes from Cancer Genetics
Kevin Eng
Thursday April 22nd 6:00-7:00 PM
TALKING POINTS
- History of the paradigm shift in cancer genetics from “one-gene” laboratories through a “big data” genomics era
- Examples of current problems in cancer genomics: Biomarker discovery through machine/statistical learning, optimization problems in the context of single-cell cell cycle phases, and regulatory network estimation.
BIO:
- Associate Professor of Oncology in Cancer Genetics and Genomics and Biostatistics and Bioinformatics, is a Computational Biologist at Roswell Park Comprehensive Cancer Center -- his very lengthy title reflects the current state of integration of data science thinking and methods with rigorous statistical thinking and cancer science problems.
- He is classically trained as a mathematical statistician with decades of experience translating for his bench-focused colleagues.
- He leads a "hybrid" laboratory with both bench scientists and computational scientists modeling the development and evolution of cancers under treatment pressure attempting to re-define the ways that we think about describing, monitoring and treating disease.
- His laboratory is focused on ovarian and prostate cancers, transcriptomics and multivariate mixture model statistics.
Download flyer here
