BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Dana-Farber Cancer Institute - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://ds.dfci.harvard.edu
X-WR-CALDESC:Events for Dana-Farber Cancer Institute
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230302T130000
DTEND;TZID=America/New_York:20230302T140000
DTSTAMP:20260421T002630
CREATED:20230224T194353Z
LAST-MODIFIED:20230308T124839Z
UID:4088-1677762000-1677765600@ds.dfci.harvard.edu
SUMMARY:Computation of High-Dimensional Penalized Generalized Linear Mixed Models
DESCRIPTION:Thursday March 2\, 2023\n1:00pm ET \nHillary Heiling\nBiostatistics PhD candidate\nUniversity of North Carolina Chapel Hill. \nAdd to calendar \nHillary’s statistical focus has primarily been in cancer-related research\, both through her graduate research assistant role in the Lineberger Comprehensive Cancer Center as well as her personal research applications. In cancer research as well as other biomedical areas\, there are issues with the replicability of results between studies. Hillary will present her work on improving the replicable selection of gene signatures for the prediction of cancer subtypes by combining data across studies and performing variable selection on generalized linear mixed models. Hillary has been able to extend the feasible dimensionality of the application to hundreds of predictors by using a factor model decomposition on the random effects\, which behaves as a dimension reduction technique. Hillary has developed software to perform this task and has published her ‘glmmPen’ R package on CRAN.
URL:https://ds.dfci.harvard.edu/event/computation-of-high-dimensional-penalized-generalized-linear-mixed-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/02/hillary-e1677267404626.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230329T130000
DTEND;TZID=America/New_York:20230329T140000
DTSTAMP:20260421T002630
CREATED:20230120T174649Z
LAST-MODIFIED:20230329T184122Z
UID:3949-1680094800-1680098400@ds.dfci.harvard.edu
SUMMARY:Dimension Reduction of Longitudinal Microbiome Data
DESCRIPTION:Frontiers in Biostatistics Seminar \nWednesday March 29\, 2023\n1:00PM Eastern Time \nPixu Shi\, PhD\nAssistant Professor of Biostatistics & Bioinformatics\nDivision of Integrative Genomics\nDepartment of Biostatistics & Bioinformatics\nDuke University School of Medicine \nAbstract:\nThe analysis of longitudinal microbiome is crucial to the understanding of how microbiome changes over time. It often requires careful maneuver of high dimensional microbial features\, missing samples and varying time points across subjects. Longitudinal microbiome data can often be formatted into a high-dimensional order-3 tensor with three modes representing the subject\, time\, and bacteria respectively. In this talk\, we present functional tensor SVD\, a dimension reduction tool that can uncover sub-population structures in subjects\, compress high-dimensional features into low-dimensional trajectories\, and extract shared temporal patterns among features\, all without imputation of missing samples or rounding of time points. We will demonstrate the robust performance of our method through simulations and multiple case studies.
URL:https://ds.dfci.harvard.edu/event/dimension-reduction-of-longitudinal-microbiome-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/01/Pixu_profile_square.jpg
END:VEVENT
END:VCALENDAR