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SUMMARY:Evaluating modules in molecular networks in light of annotation bi
 as - Charlotte Deane (University of Oxford)
DTSTART:20161110T140000Z
DTEND:20161110T150000Z
UID:TALK68925@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Many complex systems can be represented as networks and the de
 tection of novel functional modules in such networks has become an importa
 nt step in many research areas.&nbsp\; In this talk I will describe a meth
 od for evaluating potential modules that overcomes annotation biases that 
 often occur in networks.   I will demonstrate its utility in the area of b
 iological networks. In biological networks\, in the absence of gold standa
 rd functional modules\, functional annotations are often used to verify wh
 ether detected modules/communities have biological meaning. However\, as I
  will show\, the uneven distribution of functional annotations means that 
 such evaluation methods favour communities of well-studied proteins.  We p
 ropose a novel framework for the evaluation of communities as functional m
 odules.&nbsp\; Our proposed framework\, CommWalker\, takes communities as 
 inputs and evaluates them&nbsp\; in&nbsp\; their&nbsp\; local&nbsp\; netwo
 rk&nbsp\; environment&nbsp\; by&nbsp\; performing&nbsp\; short&nbsp\; rand
 om&nbsp\; walks.&nbsp\;&nbsp\; We test CommWalker&#39\;s ability to overco
 me annotation bias using input communities from four community detection m
 ethods on two protein interaction networks.&nbsp\; We find that modules ac
 cepted by CommWalker are similarly co-expressed as those accepted by curre
 nt methods.&nbsp\; Crucially\, CommWalker performs well not only in well-a
 nnotated regions\, but also in regions otherwise obscured by poor annotati
 on.&nbsp\;&nbsp\; CommWalker community prioritization both faithfully capt
 ures well-validated communities\, and identifies functional modules that m
 ay correspond to more novel biology.  If there is time at the end of the s
 eminar I will also discuss a method for network comparison that we have re
 cently developed that is aimed at identifying common organizational princi
 ples in networks. The methodology is simple\, intuitive and is applicable 
 in a wide variety of settings ranging from the functional classi&#xFB01\;c
 ation of proteins to tracking the evolution of the world trade network.  <
 br><br><br><br>
LOCATION:Seminar Room 2\, Newton Institute
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