Deep learning for predicting the response to chemical and genetic perturbations of cancer

ABSTRACT
The advances of genome sequencing and high-throughput screening have led to large-scale data resources for cancer discovery, such as The Cancer Genome Atlas (TCGA) and the Cancer Dependency Map (DepMap). Due to data heterogeneity and dimensionality, however, it remains challenging to comprehensively integrate these datasets to study the central dogma of pharmacogenomics: how multi-omics determine cellular response to perturbations. My postdoctoral research and NCI K99/R00 project focus on the development of cutting-edge deep learning models to capture and predict intricate pharmacogenomic patterns among high-dimensional genomics and high-throughput chemical and genetic screens. My talk will introduce several of our pioneering models that accurately predict cancer cells’: i) response to hundreds of approved and investigational anti-cancer drugs, and ii) genetic dependencies on a thousand potential cancer genes. The models feature specialized “transfer learning” designs that enable the translation of in vitro screens to impracticable-to-screen tumors. The studies demonstrate the exciting promise of deep learning for precision oncology by implementing an intelligent prioritization of chemical and genetic targets to enhance the efficiency and precision of drug discovery and development. 

BIO 
Yu-Chiao “Chris” Chiu is a postdoctoral fellow at the Greehey Children's Cancer Research Institute, University of Texas Health San Antonio (UTHSA). He received unique interdisciplinary training: a bachelor of science in electrical engineering, a doctorate in bioinformatics in an integrative environment of biomedical engineering and medicine, and postdoctoral training at UTHSA, which houses the NCI-designated UTHSA – MD Anderson Cancer Center. His research focuses on the development of machine and deep learning models for big genomic data to study cancer biology and improve cancer therapy. He is currently the principal investigator of two grants: an Fund for Innovation in Cancer Informatics.  

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