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Biostatistics Seminars

Department of Biostatistics Seminar Series


The Department of Biostatistics presents a regular speaker series each semester, generally on Thursday afternoon each week. Diverse experts lecture on their work in biostatistics.

Upcoming Biostats Seminars

Thu 3/1/2018 3:30PM - 4:30PM
Inference and Variable Selection with Random Forests - Lucas Mentch
Public Health Auditorium (G23)

Biostatistics guest speaker, Lucas Mentch, University of Pittsburgh, Statistics, will present, "Inference and Variable Selection with Random Forests."
Thu 4/5/2018 3:30PM - 4:30PM
Seonjoo Lee, Columbia University
Public Health Auditorium (G23)

Biostatistics guest speaker, Seonjoo Lee, Columbia University, will present.
Thu 4/12/2018 3:30PM - 4:30PM
Ciprian Crainiceanu, Johns Hopkins University
Public Health Auditorium (G23)

Biostatistics guest speaker, Ciprian Crainiceanu, Johns Hopkins University, will present.
Thu 4/19/2018 3:30PM - 4:30PM
Wensheng Guo, University of Pennsylvania
Public Health Auditorium (G23)

Biostatistics guest speaker, Wensheng Guo, University of Pennsylvania, will present.

Previous Biostats Seminars

Biostatistics Seminar Series

Biostatistics Seminar: Shuangge Steven Ma, PhD, Yale School of Public Health

Thursday 9/14 3:30PM - 5:00PM
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, Shuangge Steven Ma, PhD, Yale School of Public Health, will present, "Integrating multidimensional omics data for cancer prognosis."

Prognosis is of essential interest in cancer research. Multiple types of omics measurements – including mRNA gene expression, methylation, copy number variation, SNP, and others – have been implicated in cancer prognosis. The analysis of multidimensional omics data is challenging because of the high data dimensionality and, more importantly, because of the interconnections between different units of the same type of measurement and between different types of omics measurements. In our study, we have developed novel regularization based methods, effectively integrated multidimensional data, and constructed prognosis models. It is shown that integrating multidimensional data can lead to biological discoveries missed by the analysis of one dimensional data and superior prognosis models.

Last Updated On Tuesday, August 29, 2017 by Kapko, Bernadette E
Created On Wednesday, August 09, 2017

Contact

For information on seminars and events in the department, contact:

Bernadette Kapko
bkapko@pitt.edu
412-624-3022

© 2018 by University of Pittsburgh Graduate School of Public Health

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