Fuzzy c-means clustering with prior biological knowledge.
We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology anno...
| Published in: | Journal of Biomedical Informatics Vol. 42; no. 1; pp. 74 - 82 |
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| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
Academic Press Inc.
Feb2009
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105457375&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105457375 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Feb2009 vid: 42 iid: 1 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 105457375 NLM18595779 2010199220 10.1016/j.jbi.2008.05.009 NLM18595779 PMC2673503 105457375 ppf: 74 ppct: 8 formats: tig: atl: Fuzzy c-means clustering with prior biological knowledge. aug: au: Tari L Baral C Kim S Tari, Luis Baral, Chitta Kim, Seungchan affil: School of Computing and Informatics, Department of Computer Science and Engineering, Ira A. Fulton School of Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ 85287-8809, USA sug: subj: Cluster Analysis Genes Physiology Genetic Techniques Methods Logic Software Algorithms Biochips Bioinformatics Internet Reproducibility of Results Resource Databases Statistics Yeasts Human ab: We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike traditional clustering methods, our method is capable of assigning genes to multiple clusters, which is a more appropriate representation of the behavior of genes. Two datasets of yeast (Saccharomyces cerevisiae) expression profiles were applied to compare our method with other state-of-the-art clustering methods. Our experiments show that our method can produce far better biologically meaningful clusters even with the use of a small percentage of Gene Ontology annotations. In addition, our experiments further indicate that the utilization of prior knowledge in our method can predict gene functions effectively. The source code is freely available at http://sysbio.fulton.asu.edu/gofuzzy/. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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