Bing Zhang, Ph.D. (320x240)
Bing Zhang, Ph.D., McNair Scholar

Professor in the Department of Molecular and Human Genetics and the Lester and Sue Smith Breast Center

Appointed McNair Scholar October 2016

Dr. Zhang received his Ph.D. degree in Molecular Genetics from the Chinese Academy of Sciences followed by a postdoctoral training in bioinformatics at the Oak Ridge National Laboratory. Before joining Baylor College of Medicine in August 2016, he had been a faculty member in the Department of Biomedical Informatics at the Vanderbilt University for 10 years.

During his tenure at Vanderbilt, Dr. Zhang has established an internationally recognized research program in cancer proteogenomics, focusing on integrating genomic and proteomic data to better understand cancer biology. Innovative bioinformatics methods and tools developed by his group enabled the first integrative proteogenomic characterization of human cancer, which was published in Nature. This landmark study demonstrated that integrated proteogenomic analysis provides functional context to interpret genomic abnormalities, and that proteogenomics holds great potential to enable new advances in cancer biology, diagnostics and therapeutics.

Dr. Zhang has more than 70 publications in the areas of bioinformatics, proteomics and cancer systems biology. He has served as principal investigator, bioinformatics director, or co-investigator on more than 10 federal grants. He serves frequently as program committee member in international conferences and as reviewer for NIH study sections. He also serves on the editorial board of multiple journals including Molecular & Cellular Proteomics and Clinical Proteomics. He has been honored with local, national and international awards in recognition of his research activities, including an award from the National Library of Medicine (NLM) for Innovative Uses of NLM Information. Dr. Zhang is a funded scholar of the Cancer Prevention and Research Institute of Texas.

The long-term goal of Zhang’s research is to use proteogenomics and multi-omics data to better understand cancer biology and to improve cancer care.