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UID:20260604T010758EDT-1520BXfTvF@132.216.98.100
DTSTAMP:20260604T050758Z
DESCRIPTION:Kata Vuk\, PhD\n\nFaculty of Informatics and Data Science | Uni
 versity of Regensburg\n\nVisiting Scholar | 成人VR视频聽\n\n聽\n\nWHE
 N: Wednesday\, March 4\, 2026\, from 3:30 to 4:30 p.m.\n	WHERE: Hybrid | 20
 01 成人VR视频 College Avenue\, Rm 1140\; Zoom\n	NOTE:聽Kata Vuk will be presenti
 ng in-person at SPGH聽\n\nAbstract\n\nGenome-wide association studies (GWAS
 ) identify trait-associated loci\, but translating these signals into caus
 al genes remains statistically challenging. I present a stratified modelin
 g framework for post-GWAS gene prioritization that accounts for heterogene
 ity in variant-to-gene mechanisms. Genes are classified as regulatory\, in
 tragenic\, or mixed\, and setting-aware features are integrated within a s
 ingle random forest model. This design enables the model to learn mechanis
 m-specific patterns while maintaining comparability of predicted probabili
 ties across genes\, and it exposes substantial differences in predictive a
 ccuracy across gene settings. Applied to kidney function (eGFR)\, the fram
 ework uncovers systematic differences in the genomic features driving prio
 ritization. Locus-level analyses demonstrate stable top-gene identificatio
 n across bootstrap resampling and model comparisons.\n\nSpeaker Bio\n\nKat
 a Vuk is currently visiting 成人VR视频 as an IVADO Visiting Scholar
 . Since January 2025\, she has been supported by a fellowship from the Bav
 arian Research Institute for Digital Transformation in Germany. She is a p
 ostdoctoral researcher at the Chair of Machine Learning at the University 
 of Regensburg and received her PhD in Mathematics from Ruhr University Boc
 hum. Her research lies at the intersection of statistics and machine learn
 ing\, with a focus on interpretable and personalized modeling\, change-poi
 nt analysis\, and their applications in biostatistics\, genetics\, and bio
 medical research. \n
DTSTART:20260304T203000Z
DTEND:20260304T213000Z
SUMMARY:Stratified Modeling for Causal Gene Prioritization with Interpretab
 le Tree Ensembles
URL:/epi-biostat-occh/channels/event/stratified-modeli
 ng-causal-gene-prioritization-interpretable-tree-ensembles-371251
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