Cancer proceeds from the accumulation of genomic alterations, and develops into heterogeneous cell populations in an evolutionary process. Therefore, the prognoses of cancer patients, such as survival profile, metastasis, and drug response, are encoded by the large-volume genome data. We address the personalized medicine of cancer with machine learning and phylogenetic models, including: reliable phenotype inference of cancer through well-designed interpretable machine learning models; revealing intra-/inter-tumor heterogeneity and mechanism of tumor progression via robust deconvolution and phylogenetic algorithms; and improving prognostic prediction of cancer by incorporating machine learning and evolutionary methods.
Russell Schwartz (Chair) (Carnegie Mellon University)
Jian Ma (Carnegie Mellon University)
Xinghua Lu (University of Pittsburgh)
Adrian V. Lee (University of Pittsburgh)
Zoom Participation. See announcement.