|Awarded On||August 21, 2019|
|Title||Predicting Drug Response from Genomic Data Using Deep Learning Methods|
|Award Mechanism||Individual Investigator Research Awards for Computational Biology|
|Institution/Organization||The University of Texas Health Science Center at San Antonio|
|Principal Investigator/Program Director||Yidong Chen|
|Cancer Sites||Breast, Prostate|
*Pending contract negotiation
Technological breakthrough in Deep Learning (DL), part of Artificial Intelligent (AI), has revolutionized industries, automation and also biomedical research. While its impact starts to be felt in our daily life, whether they are AI enabled consumer products or autonomous driving cars, many of these sophisticated deep learning algorithms have not yet been adapted to harness knowledges from ever-increasing genomic data, such as those from Cancer Genome Atlas (TCGA, >10,000 tumors across 33 tumor types), Cancer Cell-Line Encyclopedia (CCLE, >1000 cell lines), and enormous therapeutic drug testing results. Recently, we have developed a novel machine learning model to predict drug response...