Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM |
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Authors: | Shengli Zhang Feng Ye Xiguo Yuan |
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Institution: | 1. Department of Mathematics , Xidian University , Xi’an , 710071 , P.R. China zhangsl@xidian.edu.cn shengli0201@163.com;3. Department of Mathematics , Xidian University , Xi’an , 710071 , P.R. China;4. School of Computer Science and Technology , Xidian University , Xi’an , 710071 , P.R. China |
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Abstract: | The accurate identification of protein structure class solely using extracted information from protein sequence is a complicated task in the current computational biology. Prediction of protein structural class for low-similarity sequences remains a challenging problem. In this study, the new computational method has been developed to predict protein structural class by fusing the sequence information and evolution information to represent a protein sample. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark data-sets, 1189 and 25PDB with sequence similarity lower than 40 and 25%, respectively. Comparison of our results with other methods shows that the proposed method by us is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity data-sets. |
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Keywords: | protein structural class principal component analysis support vector machine transition probability matrix PSSM |
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