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UID:20251027T200615EDT-3351euuKGV@132.216.98.100
DTSTAMP:20251028T000615Z
DESCRIPTION:✒️ TITLE / TITRE\n\nStatistical and computational methods for t
 he analysis of tumor heterogeneity\n\n \n\n📄 ABSTRACT / RÉSUMÉ \n\nCancer 
 is a highly heterogeneous disease\, where tumors evolve as multicellular e
 cosystems shaped by dynamic interactions among diverse cell types. This he
 terogeneity drives cancer progression but remains difficult to quantify an
 d interpret\, limiting our understanding of its role in oncogenesis. At th
 e intersection of bioinformatics\, biostatistics\, and oncology\, our work
  develops computational approaches to analyze high-dimensional\, multimoda
 l molecular data. In this talk\, I will present two examples of our method
 ological advances.\n	First\, a novel statistical framework based on mixture
  models to infer cellular heterogeneity from DNA methylation rates.\n	Secon
 d\, a high-dimensional mediation analysis showing how DNA methylation and 
 immune infiltration mediate the effect of tobacco exposure on pancreatic a
 denocarcinoma outcomes.\n	Beyond these contributions\, I will also discuss 
 our efforts to promote collaborative benchmarking and evaluation of comput
 ational algorithms through data challenge frameworks\, with the goal of bu
 ilding robust and reproducible tools for the cancer research community.\n
 \n📍 PLACE / LIEU\n	Hybride - CRM\, Salle / Room 5340\, Pavillon André Aisen
 stadt\n\n \n\nLien ZOOM Link\n
DTSTART:20251027T193000Z
DTEND:20251031T203000Z
SUMMARY:Magali Richard (CNRS\, University Grenoble-Alpes)
URL:/smerg/channels/event/magali-richard-cnrs-universi
 ty-grenoble-alpes-368587
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