• Prof. Xinghua Lu
  • Prof. Xinghua Lu
  • University of Pittsburgh, USA
  • Title: Personalized Precision Medicine for Cancers: a Big Data Approach
  • Abstract:
    Cancers are heterogeneous diseases resulted from aberrations in signal transduction pathways often caused by genomic alterations. Personalized precision medicine for cancers requires the capability of pinpointing the driving genomic alterations and targeting the aberrant pathwaysfor each individual tumor. The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium have collected unprecedented big data of cancer genomic data from over tens of thousands of tumors across all cancer types. It is uncommon that cells in a tumor host over hundreds to a thousand of genomic alterations, and it remains a challenging task to differentiate driver genomic alterations from the passenger ones at the individual tumor level. In this lecture, I will introduce a novel personalized Bayesian causal inference approach for identify driver genomic alterations through systematic mining the large number of pan-cancer tumors from TCGA. The capability of inferring driver genomic alteration in individual patients potentially will have significant impact in the era of personalized cancer care.
  • Biography:
    Dr. Xinghua Lu is a biomedical informatics researcher at the Department of Biomedical Informatics of the University of Pittsburgh. Dr. Lu has broad research experience spanning clinical patient care, basic biomedical sciences, systems biology, big data and personalized medicine. His current research covers a broad range of biomedical informatics, including systems biology modeling of cellular signal transduction pathways, semantic modeling of biomedical literatures, and translational bioinformatics. He is a co-director of the Center for Translational Bioinformatics and the Director of the Cancer Pathway Program of the Center for the Causal Discovery (funded by the Centers of Research Excellence in Big Data to Knowledge initiative of the NIH of the US). His research team has won multiple awards in international research challenges, such as the DREAM challenge and the SBV challenge. One direction of his current research concentrates on personalized medicine in big data setting, and he directs a team of collaborators to develop statistical methods for performing patient-specific inference for identifying drivers of each individual patient’s tumor through mining big data from The Cancer Genome Atlas and the International Cancer Genome Consortium. The capability of inferring driver genomic alteration in individual patients plays a fundamental role in the era of personalized precision cancer care.
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Important Dates
September 18-20, 2015
Abstract Submission Due:
April 17, 2015  >> May 29, 2015
Early Bird Registration due for Accepted Paper or Abstract: 10 days after acceptance notification

Early Bird Registration due for Audience: July 17, 2015
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