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DTSTART:20241103T020000
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DESCRIPTION:NOTE: BIS 525 students are required to attend in person. Other
 s are invited to attend in person\, but may also attend via Zoom. SPEAKER
  : Meghan Short\, PhD\, Assistant Professor of Medicine\, Tufts Universit
 y School of Medicine\; Faculty: Institute for Clinical Research and Healt
 h Policy Studies (ICRHPS)\; Faculty: Biostatistics\, Epidemiology\, and R
 esearch Design (BERD)\, Tufts Clinical and Translational Sciences Institu
 te (CTSI) TITLE : “Network analysis for understanding complex predictors 
 in complex diseases: an example from proteomics in Alzheimer's Disease" A
 BSTRACT: Weighted correlation network analysis (WGCNA) is a method for id
 entifying groups of inter-correlated features within high-dimensional dat
 asets such as gene expression and proteomic assays\, which can then be re
 lated to disease outcomes. Such network-based approaches can be used to d
 etect subtle but consistent variations in molecular profiles\, that repre
 sent specific biological pathways or processes that may be implicated in 
 health and disease. As an illustrative example\, I will discuss a piece o
 f ongoing research in Alzheimer’s Disease and Related Dementia (ADRD)\, w
 here I use WGCNA to identify groups of correlated plasma proteins in a mi
 ddle-aged\, community-based cohort from the Framingham Heart Study\, and 
 measure their associations with brain MRI measures and incident dementia.
  The analysis\, paired with an over-representation analysis using various
  annotation databases\, identifies biological processes and pathways that
  may be involved in cognitive aging. I will compare the WGCNA method with
  complementary network-based approaches for association studies using ‘om
 ics data\, which have been applied to the plasma proteome in ADRD and hav
 e identified some associations in common with the WGCNA approach.\n\nSpea
 ker:\nMeghan Short\, PhD\n\nAdmission:\nFree\n\nDetails URL:\nhttps://med
 icine.yale.edu/event/ysph-biostatistics-seminar-9-12-23/\n
DTEND;TZID=America/New_York:20230912T125000
DTSTAMP:20261022T155125Z
DTSTART;TZID=America/New_York:20230912T120000
GEO:41.302961;-72.931638
LOCATION:106-A&B\, 47 College Street\, New Haven\, CT\, United States
SEQUENCE:0
STATUS:Confirmed
SUMMARY:YSPH Biostatistics Seminar: “Network analysis for understanding co
 mplex predictors in complex diseases: an example from proteomics in Alzhe
 imer's Disease"
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