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In this study, we describe an iterative clustering approach to uncover biologically coherent structures from DNA microarray data based on a novel clustering algorithm EP_GOS_Clust.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We apply our proposed iterative algorithm to three sets of experimental DNA microarray data from experiments with the yeast <jats:italic>Saccharomyces cerevisiae<\/jats:italic> and show that the proposed iterative approach improves biological coherence. Comparison with other clustering techniques suggests that our iterative algorithm provides superior performance with regard to biological coherence. An important consequence of our approach is that an increasing proportion of genes find membership in clusters of high biological coherence and that the average cluster specificity improves.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>The results from these clustering experiments provide a robust basis for extracting motifs and trans-acting factors that determine particular patterns of expression. In addition, the biological coherence of the clusters is iteratively assessed independently of the clustering. Thus, this method will not be severely impacted by functional annotations that are missing, inaccurate, or sparse.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-9-268","type":"journal-article","created":{"date-parts":[[2008,7,1]],"date-time":"2008-07-01T06:14:18Z","timestamp":1214892858000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures"],"prefix":"10.1186","volume":"9","author":[{"given":"Meng P","family":"Tan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erin N","family":"Smith","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James R","family":"Broach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christodoulos A","family":"Floudas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2008,6,6]]},"reference":[{"key":"2253_CR1","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/nrg1749","volume":"7","author":"DB Allison","year":"2006","unstructured":"Allison DB, Cui X, Page GP, Sabripour M: Microarray data analysis: from disarray to consolidation and consensus. 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