Mingyao Li, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.
Peter Mueller, PhD, Department of Mathematics, Department of Statistics and Data Sciences, University of Texas at Austin
Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
Snehalata Huzurbazar, PhD, Department of Biostatistics, West Virginia University
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
This week's Biostatistics Seminar will feature Hongyu Zhao, PhD, Professor of Public Health (Biostatistics), of Genetics and of Statistics, Yale School of Public Health, talking on "Spatial Temporal Modeling of Gene Expression Dynamics During Brain Development".
Human neurodevelopment is a highly regulated biological process, and recent technological advances allow scientists to study the dynamic changes of neurodevelopment at the molecular level through the analysis of gene expression data from human brains. In this talk, we will focus on the analysis of data sampled from 16 brain regions in 15 time periods of neurodevelopment. We will introduce a two-step statistical inferential procedure to identify expressed and unexpressed genes and to detect differentially expressed genes between adjacent time periods. Markov Random Field (MRF) models are used to efficiently utilize the information embedded in brain region similarity and temporal dependency in our approach. We develop and implement a Monte Carlo expectation-maximization (MCEM) algorithm to estimate the model parameters. Simulation studies suggest that our approach achieves lower misclassification error and potential gain in power compared with models not incorporating spatial similarity and temporal dependency. We will also describe our methods to infer dynamic co-expression networks, spatial patterns, and ordered regulations across brain regions from these data. This is joint work with Zhixiang Lin, Ying Zhu, Can Yang, and Nenad Sestan.
Last Updated On Monday, September 26, 2016 by Haydo, Amber LC
Created On Thursday, July 14, 2016