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An Information-Theoretic Approach to the Prediction of Protein Structural Class

JOURNAL OF COMPUTATIONAL CHEMISTRY 2010年第6期31卷 1201-1206页

作者： Zheng, Xiaoqi Li, Chun Wang, Jun Shanghai Normal Univ Dept Math Shanghai 200234 Peoples R China Bohai Univ Dept Math Jinzhou 121000 Peoples R China Sci Comp Key Lab Shanghai Univ Shanghai 200234 Peoples R China

An information-theoretical approach, which combines a sequence decomposition technique and a fuzzy clustering algorithm, is proposed for prediction of protein structural class. This approach could bypass the process of selecting and comparing sequence features as done previously. First, distances between each pair of protein sequences are estimated using a conditional decomposition technique in information theory. Then, the fuzzy k-nearest neighbor algorithm is used to identify the structural class of a protein given as set of sample sequences. To verify the strength of our method, we choose three widely used datasets constructed by Chou and Zhou. It is shown by the Jackknife test that our approach represents an improvement in the prediction of accuracy over existing methods. (C) 2009 Wiley Periodicals, Inc. J Comput Chem 31: 1201-1206,2010

关键词： protein structural class prediction sequence decomposition symbol sequence complexity fuzzy k-nearest neighbor algorithm jackknife analysis

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Prediction of protein structural class using a complexity-based distance measure

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AMINO ACIDS 2010年第3期38卷 721-728页

作者： Liu, Taigang Zheng, Xiaoqi Wang, Jun Shanghai Normal Univ Dept Math Sci Comp Key Lab Shanghai Univ Shanghai 200234 Peoples R China Dalian Univ Technol Dept Appl Math Dalian 116024 Peoples R China Dalian Univ Technol Coll Adv Sci & Technol Dalian 116024 Peoples R China

Knowledge of structural class plays an important role in understanding protein folding patterns. So it is necessary to develop effective and reliable computational methods for prediction of protein structural class. To this end, we present a new method called NN-CDM, a nearest neighbor classifier with a complexity-based distance measure. Instead of extracting features from protein sequences as done previously, distance between each pair of protein sequences is directly evaluated by a complexity measure of symbol sequences. Then the nearest neighbor classifier is adopted as the predictive engine. To verify the performance of this method, jackknife cross-validation tests are performed on several benchmark datasets. Results show that our approach achieves a high prediction accuracy over some classical methods.

关键词： symbol sequence complexity Nearest neighbor algorithm Jackknife cross-validation test Performance measure

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A complexity-based method for predicting protein subcellular location

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AMINO ACIDS 2009年第2期37卷 427-433页

作者： Zheng, Xiaoqi Liu, Taigang Wang, Jun Shanghai Normal Univ Dept Math Shanghai 200034 Peoples R China Dalian Univ Technol Dept Appl Math Dalian 116024 Peoples R China Dalian Univ Technol Coll Adv Sci & Technol Dalian 116024 Peoples R China

A complexity-based approach is proposed to predict subcellular location of proteins. Instead of extracting features from protein sequences as done previously, our approach is based on a complexity decomposition of symbol sequences. In the first step, distance between each pair of protein sequences is evaluated by the conditional complexity of one sequence given the other. Subcellular location of a protein is then determined using the k-nearest neighbor algorithm. Using three widely used data sets created by Reinhardt and Hubbard, Park and Kanehisa, and Gardy et al., our approach shows an improvement in prediction accuracy over those based on the amino acid composition and Markov model of protein sequences.

关键词： Protein subcellular location symbol sequence complexity k-Nearest neighbor algorithm Jackknife analysis

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