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A Novel Fuzzy Fisher Classifier for Signal Peptide Prediction

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Abstract:

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.





More about this publication?
  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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