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This phenotyping can be applied in a supervised or an unsupervised manner. Unsupervised methods are suitable for the discovery of formerly unknown phenotypes, which are expected to occur in high-throughput RNAi time-lapse screens.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We developed an unsupervised phenotyping approach based on Hidden Markov Models (HMMs) with multivariate Gaussian emissions for the detection of knockdown-specific phenotypes in RNAi time-lapse movies. The automated detection of abnormal cell morphologies allows us to assign a phenotypic fingerprint to each gene knockdown. By applying our method to the Mitocheck database, we show that a phenotypic fingerprint is indicative of a gene\u2019s function.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Our fully unsupervised HMM-based phenotyping is able to automatically identify cell morphologies that are specific for a certain knockdown. Beyond the identification of genes whose knockdown affects cell morphology, phenotypic fingerprints can be used to find modules of functionally related genes.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-14-292","type":"journal-article","created":{"date-parts":[[2013,10,4]],"date-time":"2013-10-04T03:02:28Z","timestamp":1380855748000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Unsupervised automated high throughput phenotyping of RNAi time-lapse movies"],"prefix":"10.1186","volume":"14","author":[{"given":"Henrik","family":"Failmezger","sequence":"first","affiliation":[]},{"given":"Holger","family":"Fr\u00f6hlich","sequence":"additional","affiliation":[]},{"given":"Achim","family":"Tresch","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2013,10,4]]},"reference":[{"key":"6076_CR1","doi-asserted-by":"publisher","first-page":"R100","DOI":"10.1186\/gb-2006-7-10-r100","volume":"7","author":"AE Carpenter","year":"2006","unstructured":"Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM: Cell Profiler: image analysis software for identifying and quantifying cell phenotypes. 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