A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit <a rel="external noopener" href="https://watermark.silverchair.com/bts060.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAkcwggJDBgkqhkiG9w0BBwagggI0MIICMAIBADCCAikGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMLHaviLuaYVbmM3zEAgEQgIIB-jloN2dSdcUCRMfwHoChfl5kiUGXjyteT_0zdGixz334_xP1PGWzEPv_7XlOx9kVOOIwCtVPQu-UPFCdRA2fHdIMkz9xa-VYDaB7Ial0SS2qlQ0MlcHtKjDTvaRvgkgnR5wwmY5EtJRMmZpLrTNjObXdltDCNnZnFmqX2pZBRVViQ_NdHJNbo3qRbtx9Ru9UqVMGOcWQ4VC_o8qvJDXoDiODMsbkQIWDNeXnmIFOm2ObWAHWQVrrJ6reBM012ePkw-Ebn3gXHI4TB5roqGAHc8EKnKnt7pad65LaN_f0tJJTI6dKQ8T0BEtYhaCeL8DLPb4gGewvBDz4TumiSXEOY_t1b5I2dG3IbXfAs-QlVt_z7CmbDin6x3VCn4bFTuzjdQIEo3AjuJTI842eULdKUkgRuQ3uWVaetPWKDHL_jE4zxqsbcIzxc9LLblPIdYiJiK8JQyFAiKJCF_6Pc8EO-mgcTD8DEkg_6YUOC7idRr6tIULFjKF4vIs7OKHfgwzVwMSSqQe7YGnqMtCn-DKWNiaT_jGEyM1kj6eqwcJMjit1K7fSQQLCX1esRq3T_ZpjeAFFqntyo0SQGjRSl-iBeCWzI9SKyKerF3_2qrpx3g_TbOKVuYP5PdPveWFv0m_eSmfuvyEYdllJcHNaM-omVMuCNBcNCCFWeCMj">the original URL</a>. The file type is <code>application/pdf</code>.
Predicting kinase substrates using conservation of local motif density
<span title="2012-02-01">2012</span>
<i title="Oxford University Press (OUP)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4r72gbmtcrde5no3fwwogjs3cu" style="color: black;">Computer applications in the biosciences : CABIOS</a>
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Motivation: Protein kinases represent critical links in cell signaling. A central problem in computational biology is to systematically identify their substrates. Results: This study introduces a new method to predict kinase substrates by extracting evolutionary information from multiple sequence alignments in a manner that is tolerant to degenerate motif positioning. Given a known consensus, the new method (ConDens) compares the observed density of matches to a null model of evolution and does
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/bts060">doi:10.1093/bioinformatics/bts060</a>
<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/22302575">pmid:22302575</a>
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... not require labeled training data. We confirmed that ConDens has improved performance compared with several existing methods in the field. Further, we show that it is generalizable and can predict interesting substrates for several important eukaryotic kinases where training data is not available. Availability and implementation: ConDens can be found at
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