PI: Dr. Eric Xing, Ph.D

Co-PI: Wei Wu

Postdocs and Graduate Students:
Seunghak Lee, PhD, Computer Science Department, CMU
Micol Marchetti-Bowick, Machine Learning Department, CMU

Current Research
The objectives of Core C, Computational Genomics Core for DA (CG4DA), are to help support the FRPs and to address fundamental methodological challenges of unraveling the genetic basis of DA and medication research by a systematic inference of the mapping between genetic variations and susceptibility to DA possibly induced by certain chemical compounds. Such a mapping provides a genome-wide atlas of potential targets and their risk under chemical compounds. The three themes of the services that CG4DA proposed to provide to the FRPs and also a broader DAR community are: (i) developing machine learning methods for transcriptome-wide screening of expression traits and molecular markers for DA; (ii) genome-wide discovery of new drug targets and their epistatic genetic influences via structured association mapping; and (iii) software development for DA diagnosis, and towards guiding DA treatment.