{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:32:36Z","timestamp":1765355556595,"version":"3.38.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p><jats:p>Results: We exploit recent results in sparse graphical model learning to put forward a \u2018network clustering\u2019 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.<\/jats:p><jats:p>Availability: \u00a0go.warwick.ac.uk\/sachmukherjee\/networkclustering<\/jats:p><jats:p>Contact: \u00a0s.n.mukherjee@warwick.ac.uk<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr070","type":"journal-article","created":{"date-parts":[[2011,2,12]],"date-time":"2011-02-12T01:30:50Z","timestamp":1297474250000},"page":"994-1000","source":"Crossref","is-referenced-by-count":15,"title":["Network clustering: probing biological heterogeneity by sparse graphical models"],"prefix":"10.1093","volume":"27","author":[{"given":"Sach","family":"Mukherjee","sequence":"first","affiliation":[{"name":"1 Department of Statistics and 2Centre for Complexity Science, University of Warwick, Coventry CV4 7AL, UK"},{"name":"1 Department of Statistics and 2Centre for Complexity Science, University of Warwick, Coventry CV4 7AL, UK"}]},{"given":"Steven M.","family":"Hill","sequence":"additional","affiliation":[{"name":"1 Department of Statistics and 2Centre for Complexity Science, University of Warwick, Coventry CV4 7AL, UK"},{"name":"1 Department of Statistics and 2Centre for Complexity Science, University of Warwick, Coventry CV4 7AL, UK"}]}],"member":"286","published-online":{"date-parts":[[2011,2,10]]},"reference":[{"key":"2023012512172535500_B1","first-page":"485","article-title":"Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data","volume":"9","author":"Banerjee","year":"2008","journal-title":"J. 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