| Prediction of non-classical secreted proteins using informative physicochemical properties. | |
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MedLine Citation:
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PMID: 20658339 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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The prediction of non-classical secreted proteins is a significant problem for drug discovery and development of disease diagnosis. The characteristic of non-classical secreted proteins is they are leaderless proteins without signal peptides in N-terminal. This characteristic makes the prediction of non-classical proteins more difficult and complicated than the classical secreted proteins. We identify a set of informative physicochemical properties of amino acid indices cooperated with support vector machine (SVM) to find discrimination between secreted and non-secreted proteins and to predict non-classical secreted proteins. When the sequence identity of dataset was reduced to 25%, the prediction accuracy on training dataset is 85% which is much better than the traditional sequence similarity-based BLAST or PSI-BLAST tool. The accuracy of independent test is 82%. The most effective features of prediction revealed the fundamental differences of physicochemical properties between secreted and non-secreted proteins. The interpretable and valuable information could be beneficial for drug discovery or the development of new blood biochemical examinations. |
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Authors:
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Chiung-Hui Hung; Hui-Ling Huang; Kai-Ti Hsu; Shinn-Jang Ho; Shinn-Ying Ho |
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Publication Detail:
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Type: Journal Article Date: 2010-07-25 |
Journal Detail:
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Title: Interdisciplinary sciences, computational life sciences Volume: 2 ISSN: 1913-2751 ISO Abbreviation: Interdiscip Sci Publication Date: 2010 Sep |
Date Detail:
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Created Date: 2010-07-26 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101515919 Medline TA: Interdiscip Sci Country: Germany |
Other Details:
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Languages: eng Pagination: 263-70 Citation Subset: IM |
Affiliation:
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Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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