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UID:20250512T192944EDT-1677ZMerKr@132.216.98.100
DTSTAMP:20250512T232944Z
DESCRIPTION:Variable selection methods in high-dimensional genetic data\n\n
 Sahir Bhatnagar\, ÐãÉ«Ö±²¥\n	Tuesday February 23\, 12-1pm\n	Zoom Li
 nk: https:/mcgill.zoom.us/j/91589192037\n\nAbstract: In high-dimensional (
 HD) data\, where the number of covariates (p) greatly exceeds the number o
 f observations (n)\, estimation can benefit from the bet-on-sparsity princ
 iple\, i.e.\, only a small number of predictors are relevant in the respon
 se. This assumption can lead to more interpretable models\, improved predi
 ctive accuracy\, and algorithms that are computationally efficient. In gen
 etic studies\, where the sample sizes are small relative to the number of 
 measured features\, we must often assume a sparse model because there isn’
 t enough information to estimate p parameters. Even when the sample size i
 s large enough to estimate many parameters (e.g. 500k individuals in the U
 K Biobank)\, new challenges arise such as compute time\, memory management
  and file format. In this talk\, I introduce some popular variable selecti
 on techniques with a focus on the optimization algorithms and software imp
 lementations. I then share some recent applications of these methods for v
 ariant discovery and genetic risk prediction. If time permits\, I will end
  with an opinionated view of why the statistical methods have failed to ke
 ep up with the complexity of the data being generated.\n
DTSTART:20210223T170000Z
DTEND:20210223T180000Z
LOCATION:CA\, QC
SUMMARY:QLS Seminar Series - Sahir Bhatnagar
URL:/qls/channels/event/qls-seminar-series-sahir-bhatn
 agar-328586
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