Mukherjee, Sach and Hill, Steven (Steven M.). (2011) Network clustering : probing biological heterogeneity by sparse graphical models. Bioinformatics, Vol.27 (No.7). pp. 994-1000. ISSN 1367-4803

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Abstract

Motivation: Networks and pathways are important in describing the collective biological function of molecular players such as genes or proteins. In many areas of biology, for example in cancer studies, available data may harbour undiscovered subtypes which differ in terms of network phenotype. That is, samples may be heterogeneous with respect to underlying molecular networks. This motivates a need for unsupervised methods capable of discovering such subtypes and elucidating the corresponding network structures. Results: We exploit recent results in sparse graphical model learning to put forward a 'network clustering' approach in which data are partitioned into subsets that show evidence of underlying, subset-level network structure. This allows us to simultaneously learn subset-specific networks and corresponding subset membership under challenging small-sample conditions. We illustrate this approach on synthetic and proteomic data.

Item Type:	Journal Article
Subjects:	Q Science > Q Science (General)
Divisions:	Faculty of Science > Centre for Complexity Science Faculty of Science > Statistics
Journal or Publication Title:	Bioinformatics
Publisher:	Oxford University Press
ISSN:	1367-4803
Date:	1 April 2011
Volume:	Vol.27
Number:	No.7
Page Range:	pp. 994-1000
Identification Number:	10.1093/bioinformatics/btr070
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC), US National Cancer Institute (NCI)
Grant number:	EP/E501311/1 (EPSRC), U54 CA112970-07 (NCI)
URI:	http://wrap.warwick.ac.uk/id/eprint/41864

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