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The detection of such hot spots is considered to be an NP hard problem, but by first establishing its theoretical foundation we have been able to develop an algorithm that provides a solution.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Our results show that a first-order phase transition is observable whose critical point separates the hot-spot set from the remaining variables. Its application is also found to be more successful than existing approaches in identifying statistically significant hot-spots both with simulated data sets and in real large-scale multivariate data sets from gene arrays, electrophysiological recording and functional magnetic resonance imaging experiments.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>In summary, this new statistical algorithm should provide a powerful new analytical tool to extract the maximum information from complex biological multivariate data.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-8-331","type":"journal-article","created":{"date-parts":[[2007,9,11]],"date-time":"2007-09-11T18:13:28Z","timestamp":1189534408000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A novel approach to detect hot-spots in large-scale multivariate data"],"prefix":"10.1186","volume":"8","author":[{"given":"Jianhua","family":"Wu","sequence":"first","affiliation":[]},{"given":"Keith M","family":"Kendrick","sequence":"additional","affiliation":[]},{"given":"Jianfeng","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2007,9,11]]},"reference":[{"key":"1703_CR1","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1073\/pnas.97.1.262","volume":"97","author":"M Brown","year":"1999","unstructured":"Brown M, Grundy W, Lin D, Cristianini N, Sugnet C, Furey T, Ares M, Haussler D: Knowledge-based analysis of microarray gene expression data using support vector machines. 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