{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T01:58:56Z","timestamp":1780970336914,"version":"3.54.1"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2004,9,8]],"date-time":"2004-09-08T00:00:00Z","timestamp":1094601600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"},{"start":{"date-parts":[[2004,9,8]],"date-time":"2004-09-08T00:00:00Z","timestamp":1094601600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                        <jats:title>Background<\/jats:title>\n                        <jats:p>Clustering is one of the most commonly used methods for discovering hidden structure in microarray gene expression data. Most current methods for clustering samples are based on distance metrics utilizing all genes. This has the effect of obscuring clustering in samples that may be evident only when looking at a subset of genes, because noise from irrelevant genes dominates the signal from the relevant genes in the distance calculation.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Results<\/jats:title>\n                        <jats:p>We describe an algorithm for automatically detecting clusters of samples that are discernable only in a subset of genes. We use iteration between Minimal Spanning Tree based clustering and feature selection to remove noise genes in a step-wise manner while simultaneously sharpening the clustering.<\/jats:p>\n                        <jats:p>Evaluation of this algorithm on synthetic data shows that it resolves planted clusters with high accuracy in spite of noise and the presence of other clusters. It also shows a low probability of detecting spurious clusters. Testing the algorithm on some well known micro-array data-sets reveals known biological classes as well as novel clusters.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Conclusions<\/jats:title>\n                        <jats:p>The iterative clustering method offers considerable improvement over clustering in all genes. This method can be used to discover partitions and their biological significance can be determined by comparing with clinical correlates and gene annotations. The MATLAB<jats:sup>\u00a9<\/jats:sup> programs for the iterative clustering algorithm are available from <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/linus.nci.nih.gov\/supplement.html\">http:\/\/linus.nci.nih.gov\/supplement.html<\/jats:ext-link><\/jats:p>\n                     <\/jats:sec>","DOI":"10.1186\/1471-2105-5-126","type":"journal-article","created":{"date-parts":[[2004,9,11]],"date-time":"2004-09-11T06:23:47Z","timestamp":1094883827000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Iterative class discovery and feature selection using Minimal Spanning 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