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.
Zhiguang Huo of the Department of Biostatistics defends his dissertation on "Statistical Integrative Omics Methods for Disease Subtype Discovery"
Graduate faculty of the University and all other interested parties are invited to attend.
Disease phenotyping by omics data has become a popular approach that potentially can lead to better-personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step towards this goal. With the accumulation of massive high-throughput omics data sets, omics data integration is essential to improve statistical power and reproducibility. In this thesis, two extensions from sparse K-means method will be introduced. The first extension is towards a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype characterization. An additional pattern matching reward function guarantees consistent subtype signatures across studies. The second extension is towards integrating multi-level omics datasets with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using alternating direction method of multiplier (ADMM) will be applied for fast optimization. For both topics, simulation and real applications in breast cancer and leukemia will show the superior clustering accuracy, feature selection and functional annotation. These methods will improve statistical power, prediction accuracy and reproducibility of disease subtype discovery analysis.
Last Updated On Friday, July 07, 2017 by Valenti, Renee Nerozzi
Created On Friday, February 24, 2017