Workshops > Frontiers in Mathematical Oncology

Frontiers in Mathematical Oncology

Machine Learning for Integrating Complementary Data Sources for Cancer Pharmacogenomics and Precision Medicine

Wojciech Czaja

University of Maryland


We give an overview of state of the art methods, arising in the context of applied harmonic analysis and machine learning, which are suitable for multi-source information fusion and data analysis. These methods offer a wide range of possible applications: from selection of drug response-related genes that predict tumor-specic drug efficacy and possible mode of action, to tools that discover and experimentally validate predictive genomic determinants, to methods that assess, refine, and validate computational approaches for gene network analysis and modeling.