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Fuzzy K-Nearest Neighbor Classifier to Predict Protein Solvent Accessibility

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Neural Information Processing (ICONIP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4985))

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Abstract

The prediction of protein solvent accessibility is an intermediate step for predicting the tertiary structure of proteins. Knowledge of solvent accessibility has proved useful for identifying protein function, sequence motifs, and domains. Using a position-specific scoring matrix (PSSM) generated from PSI-BLAST in this paper, we develop the modified fuzzy k-nearest neighbor method to predict the protein relative solvent accessibility. By modifying the membership functions of the fuzzy k-nearest neighbor method by Sim et al. [1], has recently been applied to protein solvent accessibility prediction with excellent results. Our modified fuzzy k-nearest neighbor method is applied on the three-state, E, I, and B, and two-state, E, and B, relative solvent accessibility predictions, and its prediction accuracy compares favorly with those by the fuzzy k-NN and other approaches.

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Masumi Ishikawa Kenji Doya Hiroyuki Miyamoto Takeshi Yamakawa

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© 2008 Springer-Verlag Berlin Heidelberg

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Chang, JY., Shyu, JJ., Shi, YX. (2008). Fuzzy K-Nearest Neighbor Classifier to Predict Protein Solvent Accessibility. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_87

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  • DOI: https://doi.org/10.1007/978-3-540-69162-4_87

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69159-4

  • Online ISBN: 978-3-540-69162-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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