{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T19:04:54Z","timestamp":1783969494602,"version":"3.55.0"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"21","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Consensus clustering, also known as cluster ensemble, is one of the important techniques for microarray data analysis, and is particularly useful for class discovery from microarray data. Compared with traditional clustering algorithms, consensus clustering approaches have the ability to integrate multiple partitions from different cluster solutions to improve the robustness, stability, scalability and parallelization of the clustering algorithms. By consensus clustering, one can discover the underlying classes of the samples in gene expression data.<\/jats:p><jats:p>Results: In addition to exploring a graph-based consensus clustering (GCC) algorithm to estimate the underlying classes of the samples in microarray data, we also design a new validation index to determine the number of classes in microarray data. To our knowledge, this is the first time in which GCC is applied to class discovery for microarray data. Given a pre specified maximum number of classes (denoted as Kmax in this article), our algorithm can discover the true number of classes for the samples in microarray data according to a new cluster validation index called the Modified Rand Index. Experiments on gene expression data indicate that our new algorithm can (i) outperform most of the existing algorithms, (ii) identify the number of classes correctly in real cancer datasets, and (iii) discover the classes of samples with biological meaning.<\/jats:p><jats:p>Availability: Matlab source code for the GCC algorithm is available upon request from Zhiwen Yu.<\/jats:p><jats:p>Contact: \u00a0yuzhiwen@cs.cityu.edu.hk and cshswong@cityu.edu.hk<\/jats:p><jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm463","type":"journal-article","created":{"date-parts":[[2007,9,15]],"date-time":"2007-09-15T00:14:51Z","timestamp":1189815291000},"page":"2888-2896","source":"Crossref","is-referenced-by-count":143,"title":["Graph-based consensus clustering for class discovery from gene expression data"],"prefix":"10.1093","volume":"23","author":[{"given":"Zhiwen","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hau-San","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2007,9,14]]},"reference":[{"key":"2023041107263944500_","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/35000501","article-title":"Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling","volume":"403","author":"Alizadeh","year":"2000","journal-title":"Nature"},{"key":"2023041107263944500_","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511541773","author":"Baldi","year":"2002","journal-title":"DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling"},{"key":"2023041107263944500_","first-page":"31","article-title":"Ensembles based on random projections to improve the accuracy of clustering algorithms","volume":"3931","author":"Bertoni","year":"2005","journal-title":"Neural Nets, (WIRN 2005), LNCS"},{"key":"2023041107263944500_","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.artmed.2006.03.005","article-title":"Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses","volume":"37","author":"Bertoni","year":"2006","journal-title":"Artif. 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