Biostatistics Events

Biostatistics Departmental Calendar

Event
Mon 6/1/2020 11:00AM - 1:00PM
Biostatistics Dissertation Defense
Jun Zhang-Interpretable Analysis of Multivariate Functional Data-ONLINE Biostatistics Dissertation Defense
Jun Zhang-Interpretable Analysis of Multivariate Functional Data-ONLINE
Mon 6/1/2020 11:00AM - 1:00PM
** Online/Virtual Event **

Jun Zhang of the Department of Biostatistics defends her dissertation on "Interpretable Analysis of Multivariate Functional Data". 


** Online/Virtual Event **
Sat 8/1/2020 to Thu 8/6/2020
Biostatistics Conference
Joint Statistical Meetings - - JSM 2020, Philadelphia, PA Biostatistics Conference
Joint Statistical Meetings - - JSM 2020, Philadelphia, PA
Sat 8/1/2020 to Thu 8/6/2020


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.


Sun 3/14/2021 to Wed 3/17/2021
Biostatistics Conference
ENAR 2021 Spring Meeting of the International Biometric Society -- Baltimore Biostatistics Conference
ENAR 2021 Spring Meeting of the International Biometric Society -- Baltimore
Sun 3/14/2021 to Wed 3/17/2021


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.


Sat 8/7/2021 to Thu 8/12/2021
Biostatistics Conference
Joint Statistical Meetings - - JSM 2021, Seattle, WA Biostatistics Conference
Joint Statistical Meetings - - JSM 2021, Seattle, WA
Sat 8/7/2021 to Thu 8/12/2021


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.


Sun 3/27/2022 to Wed 3/30/2022
Biostatistics Conference
ENAR 2022 Spring Meeting of the International Biometric Society -- Houston Biostatistics Conference
ENAR 2022 Spring Meeting of the International Biometric Society -- Houston
Sun 3/27/2022 to Wed 3/30/2022


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.


Sat 8/6/2022 to Thu 8/11/2022
Biostatistics Conference
Joint Statistical Meetings - - JSM 2022, Washington, DC Biostatistics Conference
Joint Statistical Meetings - - JSM 2022, Washington, DC
Sat 8/6/2022 to Thu 8/11/2022


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.


Recent Events

Biostatistics Dissertation Defense

Li Zhu - Bayesian variable selection model and differential co-expression network analysis for...

Tuesday 4/9 12:00PM - 2:00PM
A216 Public Health

Li Zhu of the Department of Biostatistics defends her dissertation on "Bayesian variable selection model and differential co-expression network analysis for multi-omics data integration".

Committee Chairperson: George C. Tseng, ScD, Department of Biostatistics

Committee Members: 
Robert Krafty, PhD, Department of Biostatistics
Lu Tang, PhD, Department of Biostatistics
Daniel E. Weeks, PhD, Department of Human Genetics 
Wei Chen, Department of Pediatrics 
 

Graduate faculty of the University and all other interested parties are invited to attend


ABSTRACT:

Due to the large accumulation of omics data sets in public repository, innumerable studies have been designed to analyze omics data for various purposes. However, the analysis of single data set often suffers from limited sample size, small power, and lack of reproducibility across studies, and thus data integration is gaining more and more attention nowadays. This dissertation focuses on developing methods for variable selection in regression and clustering for multi-omics data integration, and identification of differential co-expression network in the transcriptomic meta-analysis setting.

In the first paper, we propose a Bayesian indicator variable selection model to incorporate multi-layer overlapping group structure (MOG) in the regression setting, motivated by the structure commonly encountered in multi-omics applications, in which a biological pathway contains tens to hundreds of genes and a gene can contain multiple experimentally measured features (such as its mRNA expression, copy number variation and methylation levels of possibly multiple sites). We evaluated the model in simulations and two breast cancer examples, and demonstrated that the result not only enhances prediction accuracy but also improves variable selection and model interpretation that lead to deeper biological insight of the disease. In the second paper, we extended MOG to Gaussian mixture models for clustering, aiming to identify disease subtypes and detect subtype-relevant omics features.

In the third paper, we present a meta-analytic framework for detecting differential co-expression networks (MetaDCN). Differential co-expression (DC) analysis, different from conventional differential expression (DE) analysis, helps detect alterations of gene-gene correlations in case/control comparison, which is likely to be missed in DE analysis.

Public health significance: Methods proposed in paper 1 and 2 not only can predict disease outcome or identify disease subtypes, but also determine relevant biomarkers, which can potentially facilitate the design of a test assay to monitor disease progression, predict disease subtypes, and guide treatment decision. Methods developed in paper 3 provides a novel framework to identify differentially co-expressed genes to help us better understand how gene-gene interactions are altered in disease mechanism and provide potential new molecular targets for drug development.

Last Updated On Monday, September 23, 2019 by Valenti, Renee Nerozzi
Created On Tuesday, March 19, 2019

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