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

Zhou Fang - Integration and Missing Data Handling in Multiple Omics Studies

Thursday 5/3 9:00AM - 11:00AM
7139 Public Health, Peterson Seminar Room

Zhou "Ark" Fang of the Department of Biostatistics defends his dissertation on "Integration and Missing Data Handling in Multiple Omics Studies".

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


ABSTRACT:

In modern multiple omics high-throughput data analysis, data integration and missingness data handling are common problems in discovering regulatory mechanisms associated with complex diseases and boosting power and accuracy. Moreover, in genotype imputation, or genotyping problem, the integration of linkage disequilibrium (LD) and identity-by-descent (IBD) information becomes essential to reach universal superior performance. In pathway analysis, when multiple studies of different conditions are jointly analyzed, simultaneous discovery of differential and consensual pathways, and reducing pathway redundancy introduced by combining public pathway databases, is valuable for knowledge discovery. This dissertation focus on the development of a Bayesian multi-omics data integration model with missingness handling, a novel genotype imputation methods incorporating both LD and IBD information, and a comparative pathway analysis integration method.  

In the first chapter of this dissertation, inspired by the popular Integrative Bayesian Analysis of Genomics data (iBAG), we propose a full Bayesian model that allows incorporation of samples with missing omics data. Simulation results show improvement of the new full Bayesian approach in terms of outcome prediction accuracy and feature selection performance when sample size is limited and proportion of missingness is large. However, when sample size is large or the proportion of missingness is low, incorporating samples with missingness may introduce extra inference uncertainty. Therefore we also propose a self-learning cross-validation (CV) scheme to facilitate imputation decisions. Simulations and a real application on child asthma dataset demonstrate superior performance of the CV decision scheme when various types of missing mechanisms are evaluated. 

In the second chapter, we propose a novel genotype inference method, namely LDIV, to integrate both LD and IBD information. To evaluate our approach, we simulated individuals in different family structures, with variants of all rarity sequenced in a wide range of depth. Simulation and real data results showed that with an informative family structure, LDIV could significantly increase the genotype accuracy across variants with different rarity.  

The third chapter presents a meta-analytic integration tool, Comparative Pathway Integrator (CPI), to discover consensual and differential enrichment patterns using adaptively weighted Fisher method, reduce pathway redundancy by consensus clustering, and assist explanation of the pathway clusters with a novel text mining algorithm. We applied CPI to jointly analyze six psychiatric disorder transcriptomic studies to demonstrate its effectiveness, and found functions confirmed by previous biological studies as well novel enrichment patterns.

Last Updated On Tuesday, October 9, 2018 by Valenti, Renee Nerozzi
Created On Monday, April 9, 2018

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