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

Tianzhou Ma: Differential Expression and Feature Selection in the Analysis of Multiple Omics Studies

Friday 3/2 2:00PM - 4:00PM
7139 Public Health, Peterson Seminar Room

Tianzhou "Charles" Ma of the Department of Biostatistics defends his dissertation on "Differential Expression and Feature Selection in the Analysis of Multiple Omics Studies". 

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


ABSTRACT:

With the rapid advances of high-throughput technologies in the past decades, various kinds of omics data have been generated from many labs and accumulated in the public domain. These studies have been designed for different biological purposes, including the identification of differentially expressed genes between two conditions, the selection of important biomarkers that can predict a clinical outcome, etc.  Effective meta-analysis of omics data from multiple studies can improve statistical power, accuracy and reproducibility of single study. This dissertation covered a few methods for differential expression (section 1) and feature selection (section 2) in the analysis of multiple omics studies.  
In the first section, we proposed a full Bayesian hierarchical model for RNA-seq meta-analysis by modeling count data, integrating information across genes and across studies, and modeling differential signals across studies via latent variables. A Dirichlet process mixture prior is further applied on the latent variables to provide categorization of detected biomarkers according to their differential expression patterns across studies. We used both simulations and a real application on multiple brain region HIV-1 transgenic rats to demonstrate improved sensitivity, accuracy and biological findings of our method. In a follow-up paper, we extended the previous Bayesian model to jointly integrate transcriptomic data from the two platforms: microarray and RNA-seq. 
In the second section, we considered a general framework for variable screening with multiple omics studies and further proposed a novel two-step screening procedure for high-dimensional regression analysis in this framework. Compared to the one-step procedure and rank-based sure independence screening procedure, our procedure greatly reduced false negative errors while keeping a low false positive rate. Theoretically, we showed that our procedure possesses the sure screening property with weaker assumptions on signal strengths and allows the number of features to grow at an exponential rate of the sample size.
Detection of important biomarkers that are either differentially expressed or predictive of clinical outcomes is essential for searching for potential drug targets and understanding the disease mechanism. Such findings in basic science can be translated into preventive medicine or potential treatment for disease to promote human health and improve the global healthcare system.

Last Updated On Tuesday, July 10, 2018 by Valenti, Renee Nerozzi
Created On Wednesday, February 21, 2018

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