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SUMMARY:Inferring gene-gene associations and gene networks beyond standard
  statistical models - Haiyan Huang (University of California\, Berkeley)
DTSTART:20160712T123000Z
DTEND:20160712T130000Z
UID:TALK66716@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:With the advent of high-throughput technologies making large-s
 cale gene  expression data readily available\, developing appropriate comp
 utational tools to  infer gene interactions has been a major challenge in 
 systems biology. In this  talk\, I will discuss two methods of finding gen
 e associations that differ in  their considerations of how genes behave ac
 ross the given samples. The first  method applies to the case where the pa
 tterns of gene association may change or  only exist in a subset of all th
 e samples. The second method goes beyond  pairwise gene relationships to h
 igher level group interactions\, but requiring  similar gene behaviours ac
 ross all the samples. We compare both methods to other  popular approaches
  using simulated and real data\, and demonstrate they lead to  better gene
 ral performance and capture important biological features in certain  situ
 ations that are missed by the other methods.
LOCATION:Seminar Room 1\, Newton Institute
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