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Our approach consists of two analytical steps of the metabolic profiling data, an initial non-linear unsupervised analysis with Self-Organizing Maps (SOM) to identify similarities and differences among the metabolic profiles of the studied strains, followed by a second, supervised analysis for training a classifier based on the selected biomarkers. Our analysis identified seven putative biomarkers that were able to cluster the samples according to their genotype. A Support Vector Machine was subsequently employed to construct a predictive model based on the seven biomarkers, capable of distinguishing correctly 14 out of the 16 samples of the different <jats:italic>A. nidulans<\/jats:italic> strains.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Our study demonstrates that it is possible to use metabolite profiling for the classification of filamentous fungi as well as for the identification of metabolic engineering targets and draws the attention towards the development of a common database for storage of metabolomics data.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-9-59","type":"journal-article","created":{"date-parts":[[2008,2,20]],"date-time":"2008-02-20T19:13:47Z","timestamp":1203534827000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification"],"prefix":"10.1186","volume":"9","author":[{"given":"Irene","family":"Kouskoumvekaki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Svava \u00d3","family":"J\u00f3nsd\u00f3ttir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lisbeth","family":"Olsson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gianni","family":"Panagiotou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2008,1,28]]},"reference":[{"key":"2044_CR1","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.1158\/0008-5472.CAN-04-3506","volume":"65","author":"EM Reis","year":"2005","unstructured":"Reis EM, Ojopi EPB, Alberto FL, Rahal P, Tsukumo F, Mancini UM, Guimaraes GS, Thompson GMA, Camacho C, Miracca E, Carvalho AL, Machado AA, Paquola ACM, Cerutti JM, da Silva AM, Pereira GG, Valentini SR, Nagai MA, Kowalski LP, Verjovski-Almeida S, Tajara EH, Dias-Neto E: Consortium HNA: Large-scale transcriptome analyses reveal new genetic marker candidates of head, neck and thyroid cancer. 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