Network clustering : probing biological heterogeneity by sparse graphical models
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 1367-4803Full text not available from this repository.
Official URL: http://dx.doi.org/10.1093/bioinformatics/btr070
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|
|Official Date:||1 April 2011|
|Page Range:||pp. 994-1000|
|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)|
Actions (login required)