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Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We developed <jats:italic>TargetSpy<\/jats:italic>, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes <jats:italic>TargetSpy<\/jats:italic> suitable for analyzing unconserved genomic sequences.<\/jats:p>\n            <jats:p>In order to allow for an unbiased comparison of <jats:italic>TargetSpy<\/jats:italic> to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. <jats:italic>TargetSpy<\/jats:italic> predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In <jats:italic>Drosophila melanogaster<\/jats:italic> not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that <jats:italic>TargetSpy<\/jats:italic> predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Only a few algorithms can predict target sites without demanding a seed match and <jats:italic>TargetSpy<\/jats:italic> demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. <jats:italic>TargetSpy<\/jats:italic> was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.targetspy.org\" ext-link-type=\"uri\">http:\/\/www.targetspy.org<\/jats:ext-link>.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-11-292","type":"journal-article","created":{"date-parts":[[2010,5,28]],"date-time":"2010-05-28T18:16:21Z","timestamp":1275070581000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":153,"title":["TargetSpy: a supervised machine learning approach for microRNA target prediction"],"prefix":"10.1186","volume":"11","author":[{"given":"Martin","family":"Sturm","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Hackenberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Langenberger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dmitrij","family":"Frishman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2010,5,28]]},"reference":[{"issue":"5","key":"3749_CR1","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1016\/0092-8674(93)90529-Y","volume":"75","author":"RC Lee","year":"1993","unstructured":"Lee RC, Feinbaum RL, Ambros V: The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. 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