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SUMMARY:Learning microRNA regulatory networks from genomic sequence and ex
 pression data - Jim Huang\, University of Toronto
DTSTART:20061107T150000Z
DTEND:20061107T160000Z
UID:TALK5850@talks.cam.ac.uk
CONTACT:Oliver Williams
DESCRIPTION:MicroRNAs (miRNAs) regulate a large proportion of mammalian ge
 nes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating 
 their translation into protein. Although much work has been done in the ge
 nome-wide computational prediction of miRNA genes and their target mRNAs\,
  an open question is how to efficiently obtain functional miRNA targets fr
 om a large number of candidates. Here\, I propose a novel Bayesian model a
 nd learning algorithm\, GenMiR++ (Generative model for miRNA regulation)\,
  that accounts for patterns of gene expression using miRNA expression data
  and a set of candidate miRNA targets. A set of high-confidence functional
  miRNA targets are then obtained from the data using a variational Bayesia
 n learning algorithm. The learning algorithm detects 467 functional miRNA 
 targets out of 1\, 770 targets obtained from TargetScanS in mouse at a fal
 se detection rate of 2.5%: several confirmed miRNA targets appear in our h
 igh-confidence set\, such as the interaction miR-16 and BCL2\, an anti-apo
 ptotic gene which has been implicated in chronic lymphocytic leukaemia. I 
 will present results on the robustness of our model showing that the learn
 ing algorithm is not sensitive to various perturbations of the data. The s
 et of GenMiR++ functional targets represent a significant increase in the 
 number of miRNA targets and represent a starting point for a global unders
 tanding of gene regulation. \nJoint work with Quaid Morris and Brendan Fre
 y.\n
LOCATION:Small public lecture room\, Microsoft Research Ltd\, 7 J J Thomso
 n Avenue (Off Madingley Road)\, Cambridge
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