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A novel fuzzy Fisher classifier for signal peptide prediction
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نویسنده
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gao c.-f. ,qiu z.-x. ,wu x.-j. ,tian f.-w. ,zhang h. ,chen w.
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منبع
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protein and peptide letters - 2011 - دوره : 18 - شماره : 8 - صفحه:831 -838
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چکیده
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Signal peptides recognition by bioinformatics approaches is particularly important for the efficient secretion and production of specific proteins. we concentrate on developing an integrated fuzzy fisher clustering (iffc) and designing a novel classifier based on iffc for predicting secretory proteins. iffc provides a powerful optimal discriminant vector calculated by fuzzy intra-cluster scatter matrix and fuzzy inter-cluster scatter matrix. because the training samples and test samples are processed together in iffc,it is convenient for users to employ their own specific samples of high reliability as training data if necessary. the cross-validation results on some benchmark datasets indicate that the fuzzy fisher classifier is quite promising for signal peptide prediction. © 2011 bentham science publishers ltd.
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کلیدواژه
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Fuzzy Fisher clustering; Fuzzy scatter matrix; Optimal discriminant vector; Secretory protein recognition; Signal peptides
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آدرس
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jiangsu provincial key laboratory of asic,nantong university,nantong 226019,china,school of computer science and technology,jiangnan university, China, jiangsu provincial key laboratory of asic,nantong university,nantong 226019,china,school of mechanical engineering,nantong university,nantong, China, school of computer science and technology,jiangnan university, China, state key laboratory of food science and technology,jiangnan university,wuxi 214122,china,school of food science and technology,jiangnan university, China, state key laboratory of food science and technology,jiangnan university,wuxi 214122,china,school of food science and technology,jiangnan university, China, state key laboratory of food science and technology,jiangnan university,wuxi 214122,china,school of food science and technology,jiangnan university, China
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Authors
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