Seminar Series

The Applied Biostatistics Seminar Series consists of a series of talks with the primary purpose of furthering statistical knowledge on an applied level. Talks will focus on advances in biostatistical methods and statistical programming techniques and their translation into addressing biomedical research questions. The seminars are open to all members of the Duke community, but primarily geared toward applied statistical researchers.

Learning interpretable representations of biological data

Maria Chikina, PhD
Wednesday, January 31, 2018 - 10:00 at MSRB 1 Room 001 Ground level Classroom

Abstract: The increasing ease of collecting genome-scale data has rapidly accelerated its use in all areas of biomedical science. Translating genome scale data in to testable hypothesis, on the other hand, is challenging and remains an active area method development. In this talk we present two machine learning approaches to...
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Probabilistic graphical models in cancer genomics applications

Jie Liu, PhD
Monday, January 29, 2018 - 11:00 at Hock Plaza 8th Floor #8065

Probabilistic graphical models are powerful machine learning methods, and can be used in many cancer genomics problems. In this talk, I will discuss the application of graphical models to two cancer genomics problems: tumor heterogeneity analysis and cancer genome-wide association studies. For tumor heterogeneity analysis, we design a graphical model...
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Systems neuroscience beyond the tuning curve.

Dr. John Pearson
Thursday, January 25, 2018 - 10:30 at MSRB I Room #001 203 Research Drive

Abstract: Although the idea of the tuning curve as a summary of neural function has dominated half a century of systems neuroscience, its application in cognitive domains and to the prefrontal cortex has not yet produced the same sort of compelling picture as in vision and motor systems. This talk...
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De novo detection and accurate inference of differentially methylated regions

Keegan Korthauer
Wednesday, January 24, 2018 - 10:00 at Jones Building Room 143

Abstract: With recent advances in sequencing technology, it is now feasible to measure DNA methylation at tens of millions of sites across the genome. A fundamental task in the analysis of methylation sequencing data is to detect differentially methylated regions, composed of multiple sites with differing methylation levels among populations...
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Theory Informs Practice: Smoothing Parameters Selection for Smoothing Spline ANOVA Models in Large Samples

Xiaoxiao Sun
Monday, January 22, 2018 - 02:00 at Hock Plaza 8th Floor #8065

Abstract:Large samples have been generated routinely from various sources. Classic statistical models, such as smoothing spline ANOVA models, are not well equipped to analyze such large samples due to expensive computational costs. In particular, the daunting computational costs of selecting smoothing parameters render the smoothing spline ANOVA models impractical. In...
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