Professor of Medicine
Adjunct Professor of Bioengineering & Computer Science
UC San Diego
Director, Cancer Cell Map Initiative
Director, National Resource for Network Biology
Director, San Diego Center for Systems Biology
B.S. in Electrical Engineering and Computer Science, M.I.T. 1994
M.Eng. in Electrical Engineering and Computer Science, M.I.T. 1995
Ph.D. in Molecular Biotechnology, University of Washington 2001
About Dr. Trey Ideker
Trey Ideker, Ph.D. is a Professor in the Departments of Medicine, Bioengineering and Computer Science at UC San Diego, and Director or co-Director of three NIH-supported research centers: the NIGMS National Resource for Network Biology, the NCI Cancer Cell Map Initiative, and the NIMH Psychiatric Cell Map Initiative. Dr. Ideker received Bachelor’s and Master’s degrees from MIT in Electrical Engineering and Computer Science and his Ph.D. from the University of Washington in Molecular Biology under the supervision of Dr. Leroy Hood. Ideker is a pioneer in using genome-scale measurements to construct network models of cellular processes and disease and has founded software tools including the Cytoscape ecosystem for biological network analysis, which has been cited >13,000 times. Ideker serves on the Editorial Boards for Cell, Cell Reports, Molecular Systems Biology, and PLoS Computational Biology and is a Fellow of AAAS and AIMBE. He was named a Top 10 Innovator by Technology Review and was the recipient of the Overton Prize from the International Society for Computational Biology. His work has been featured in news outlets such as The Scientist, San Diego Union-Tribune, Forbes magazine, NPR, and The New York Times.
About the Ideker Lab
The long-term objective of the Ideker Laboratory is to create artificially intelligent models of cancer and other diseases for translation of patient data to precision diagnosis and treatment. We seek to advance this goal by addressing fundamental questions in systems biology and bioinformatics, including: What are the genetic and molecular networks that promote cancer, and how can we best chart these? How do we use knowledge of these networks in intelligent systems for translation of genotype to phenotype?