Event Summary:
A one-day workshop organized by the Center for Interdisciplinary Research and
Consulting at the University of Maryland, Baltimore County (UMBC), and the
Center for Innovative Study Design (CISD) at Stanford University in the field of Clinical Trials and Regulatory Science.
Registration is now closed
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Organizing Committee:
Yi Huang (UMBC)
Nagaraj Neerchal (UMBC)
Bimal Sinha (UMBC)
Tze Lai (Stanford)
Phillip Lavori (Stanford)
Ying Lu (Stanford)
Jie Chen (Novartis)
Joseph Heyse (Merck)
For more information:
Please contact Dr. Yi Huang, Department of Mathematics and
Statistics, University of Maryland Baltimore County (UMBC),
1000 Hilltop Circle, Baltimore, MD 21250.
E-Mail: yihuang@umbc.edu.
For any Technical difficulties Please contact Zana Coulibaly at czana1@umbc.edu.
Participant Information
Chuanhua Julia Xing from XPrecision.
Paper: Bayesian Model for Inferring Joint Effects of High Dimensional Biomarkers from Genetic Interaction Networks
The identification of the joint effects of high dimensional biomarkers has become increasingly important in deciphering complex diseases and drug development nowadays, as most of current discoveries have turned out to be false positives by considering only single risk factors. We propose Bayesian integrative tensor (BIT) models, which can capture the high dimensional interactions and therefore the joint effects, motivated by a high dimensional genetic interaction study of dietary fat and bone density. Relative to other approaches, such as directly parameterizing interactions in regression models, our proposed approach has considerable advantages in scaling up to analysis simultaneously considering thousands of single nucleotide polymorphisms and environmental factors, while limiting false positives. The proposed models accommodate probabilistic factorization for mixed scale variables, while developing conditional mutual information-based methods for identifying significant interaction networks. The joint effects of high dimensional disease risk factors/biomarkers can therefore be derived from the interaction networks. The methods are assessed in simulation studies, and applied to extract genetic risk factors from our motivating dietary fat and bone mineral density data.