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DTSTART:19700308T020000
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DTSTAMP:20210402T160552Z
LOCATION:Track 3
DTSTART;TZID=America/New_York:20201113T154000
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UID:submissions.supercomputing.org_SC20_sess222_ws_cafcw121@linklings.com
SUMMARY:A Metapath Approach to Predicting Drug Response in Cancer Cell Lin
 es
DESCRIPTION:Workshop\n\nA Metapath Approach to Predicting Drug Response in
  Cancer Cell Lines\n\nCohn\n\nWhen applying machine learning in cancer res
 earch and, in particular, when trying to predict drug response in cancer c
 ell lines or other experimental systems, an ongoing problem is the relativ
 ely small number of samples when compared to the possible number of featur
 es.   This is frequently compounded with a large amount of noise in the da
 ta, a lack of uniformity in the way experiments are conducted across labs,
  and an over-reliance on expression data.\n\nMetapath analysis is a comput
 ational approach to similarity search which takes advantage of the links a
 mong nodes in a heterogenous data network to encode model features.  Publi
 shed examples of this approach include a number of subject domains, includ
 ing exploration of disease-associated genes.  The base calculation for enc
 oding metapath features is the path degree product (PDP), which is an indi
 cation of the number and length of paths through the data network between 
 nodes of interest. \n\nIn this presentation, we show that xgboost models i
 ncluding metapath-encoded features appear to perform significantly better 
 than models using expression alone or the same features encoded with one-h
 ot encoding.  In addition, these models appear to produce more robust feat
 ure selection.\n\nRegistration Category: Workshop Reg Pass
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