hupo2017@conferencepartners.ie

Bing Zhang

Bing Zhang, Ph.D.

Talk title: Democratizing cancer proteogenomic data

Bing Zhang, Ph.D.
Professor
Department of Molecular and Human Genetics
Baylor College of Medicine
Houston, TX 77030, USA

Bio

Dr. Zhang is Professor of Molecular and Human Genetics at the Baylor College of Medicine in Houston, Texas. He received his PhD degree in 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 ten years. Dr. Zhang’s research program focuses on integrating genomic and proteomic data to better understand cancer biology. He has more than 80 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 ten federal grants. He serves frequently as program committee member in international conferences and reviewer for NIH panels. He also serves on the editorial board of Molecular & Cellular Proteomics and Clinical Proteomics.

Abstract

Large-scale cancer omics projects such as TCGA and CPTAC have produced a vast amount of genomic and proteomic data. To fully realize the potential of these data, we will need to make them directly available and usable to the entire cancer research community. In this talk, I will introduce two tools for democratizing cancer proteogenomic data: LinkedOmics and PepQuery. LinkedOmics (http://www.linkedomics.org) is a web platform for exploring associations between different types of molecular and clinical attributes, comparing associations discovered from different omics platforms or tumor types, and interpreting identified associations in the context of biological pathways and molecular networks. PepQuery is a peptide-centric search engine that makes mass spectrometry proteomics data directly available for validating novel peptides predicted from genomic studies.

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