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SUMMARY:Veridical Data Science for biomedical discovery: detecting epistat
 ic interactions with epiTree - Professor Bin Yu (UC Berkeley)
DTSTART;VALUE=DATE-TIME:20210218T153000Z
DTEND;VALUE=DATE-TIME:20210218T163000Z
UID:https://talks.ox.ac.uk/talks/id/1a65cc08-dd84-441c-80be-13ab54f4b007/
DESCRIPTION:“A.I. is like nuclear energy — both promising and dangerou
 s” — Bill Gates\, 2019.\n\nData Science is a pillar of A.I. and has dr
 iven most of recent cutting-edge discoveries in biomedical research. In pr
 actice\, Data Science has a life cycle (DSLC) that includes problem formul
 ation\, data collection\, data cleaning\, modeling\, result interpretation
  and the drawing of conclusions. Human judgement calls are ubiquitous at e
 very step of this process\, e.g.\, in choosing data cleaning methods\, pre
 dictive algorithms and data perturbations. Such judgment calls are often r
 esponsible for the “dangers” of A.I. To maximally mitigate these dange
 rs\, we developed a framework based on three core principles: Predictabili
 ty\, Computability and Stability (PCS). Through a workflow and documentati
 on (in R Markdown or Jupyter Notebook) that allows one to manage the whole
  DSLC\, the PCS framework unifies\, streamlines and expands on the best pr
 actices of machine learning and statistics – bringing us a step forward 
 towards veridical Data Science.\nIn this lecture\, we will illustrate the 
 PCS framework through the epiTree\; a pipeline to discover epistasis inter
 actions from genomics data. epiTree addresses issues of scaling of penetra
 nce through decision trees\, significance calling through PCS p-values\, a
 nd combinatorial search over interactions through iterative random forests
  (which is a special case of PCS). Using UK  Biobank data\, we validate th
 e epiTree pipeline through an application to the red-hair phenotype\, wher
 e several genes are known to display epistatic interactions.\nSpeakers:\nP
 rofessor Bin Yu (UC Berkeley)
LOCATION:Venue to be announced
TZID:Europe/London
URL:https://talks.ox.ac.uk/talks/id/1a65cc08-dd84-441c-80be-13ab54f4b007/
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DESCRIPTION:Talk:Veridical Data Science for biomedical discovery: detectin
 g epistatic interactions with epiTree - Professor Bin Yu (UC Berkeley)
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