报告题目：Big Data to High Dimensional Feature Selection and Survival Prediction by Maximizing ROC Utility Functions and Functional Modeling
报告人：Prof. Ming T TanCHAIR, BIOSTATISTICS & BIOINFORMATICSPROFESSOR OF BIOSTATISITCS AND BIOINFORMARTICSLombardi Comprehensive Cancer Center (LCCC)
报告时间：2016.6.14 上午10：00
报告地点：逸夫科技馆3楼生物电子学国家重点实验室会议室
欢迎各位老师和同学参加，谢谢！
报告摘要：Statistical learning models have been utilized in analysis of omics based molecular signatures predictive of patient outcomes including survival. The first predictive model is the regularized regression model. In supervised learning or disease classification, most standard methods, however, are designed to maximize the overall accuracy and cannot incorporate different costs to different classes explicitly. We proposed a novel regularized model by explicitly maximizing a relevant function of the receiver operating characteristic curve, e.g., a weighted specificity and sensitivity or an L1 penalized global AUC maximization. Experimental results with chemotherapy and large genomics data demonstrate that the proposed procedures can be used for identifying important genes and pathways that are related to survival and for building a parsimonious model for predicting survival of future patients. The second predictive analysis development is a functional model that takes advantage of the serial measurements of immune response parameters to predict patient survival. The talk concludes with a discussion of and a summary of current and future work including integrated genomics analysis.
报告人简介：Dr. Tan has longstanding research interests in methods for the design, monitoring and analysis of clinical trials and predictive analytics in clinical trials and Big data. One of his current research foci is statistical methods for searching and evaluating multi-drug combinations utilizing both experimental data and system biology, innovative methods to optimally design and efficiently analyze pre-clinical drug combination therapies in cancer by integrating concepts in modern statistical and number-theoretic methods and pharmacology; and high dimensional genomics data analysis in Cancer Epidemiology, all funded by R01 grants from the NCI and NHLBI.
Dr. Tan also has extensive collaborative research experience in the design, conduct and analysis of clinical trials (in both multi-center and single institutional settings), laboratory investigations, biomarker evaluation, genomics and epidemiological research.
Dr. Tan has served on multiple NIH panels and scientific review boards, Data and Safety Monitoring Boards, and FDA Advisory Committees. He is an elected Fellow of the American Statistical Association and an elected Member of the International Statistical Institute. Dr. Tan is current Associate Editor of Statistics in Medicine and Drug Design, Development and Therapy, and has served on the editorial board for Biometrics.
Before joined Georgetown in the fall of 2012, he was professor of Epidemiology and Public Health and Head of the Division of Biostatistics and Bioinformatics of the University of Maryland School of medicine and Marlene and Stewart Greenebaum Cancer Center (UMGCC) (2002-2012). He was previously a senior member (faculty) at St. Jude Children's Research Hospital Cancer Center and biostatistics director of St Jude's Developmental Therapeutics for Solid Malignancies Program (1997-2002), assistant and associate staff/professor of Biostatistics and Epidemiology at the Cleveland Clinic (1990-1997).
联系人：谢建明