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SUMMARY:CNN Seminar - December - Francesco Iorio (European Bioinformatics 
 Institute &amp\; Sanger Institute) and Florian Markowetz (Cancer Research 
 UK Cambridge Research Institute)
DTSTART:20111214T143000Z
DTEND:20111214T153000Z
UID:TALK34845@talks.cam.ac.uk
CONTACT:Petra Vertes
DESCRIPTION:Our two talks this month are:\n\n*Francesco Iorio:*\n_Drug Dis
 covery and Re-purposing by Network-Analysis of Gene Expression Data_\n\nA 
 significant number of recent studies wink at the idea that every biologica
 l state can be described by a proper gene expression signature: a well def
 ined set of genes together with a pattern of expression that is exclusivel
 y linked to it. The underlying concepts are: i) any condition\, for exampl
 e\, the activity of a given pathway\, a disease phenotype or cellular resp
 onse to drug treatment\, realizes some change in transcriptional activity\
 ; ii) even if a single gene on its own poorly characterizes a biological s
 tate of interest\, this ability is significantly improved when considering
  a set of genes with their combined pattern of expression.\n\nI will descr
 ibe MANTRA (Mode of Action by NeTwoRk Analysis): a computational tool for 
 the analysis of the Mode of Action (MoA) of novel drugs and the identifica
 tion of known and approved candidates for "drug repositioning"\, combining
  the introduced ideas with simple concepts from network-theory and non-par
 ametric statistics.\n\n\n*Florian Markowetz:*\n_Inferring phenotypic netwo
 rks with Nested Effect Models_\n\nIn high-dimensional phenotyping screens\
 , a large number of cellular features is observed after perturbing genes b
 y knockouts or RNA interference. Comprehensive analysis of perturbation ef
 fects is one of the most powerful techniques for attributing functions to 
 genes. I will talk about Nested Effects Models\, a probabilistic method to
  efficiently infer a genetic hierarchy from the nested structure of observ
 ed perturbation effects. These hierarchies elucidate the structures of sig
 naling pathways and regulatory networks. Our methods achieve two goals: (1
 ) they reveal clusters of genes with highly similar phenotypic profiles\, 
 and (2) they order (clusters of) genes according to subset relationships b
 etween phenotypes. In contrast to other graphical models\, Nested Effect M
 odels are not built on conditional independence\, but subset relations - m
 aking them attractive from a theoretical and applied perspective.
LOCATION:Keynes Hall in Kings College
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