A Novel Method for Prediction of Protein Domain Using Distance-Based Maximal Entropy |
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Authors: | Shu-xue Zou Yan-xin Huang Yan Wang Chun-guang Zhou |
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Institution: | Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China |
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Abstract: | Detecting the boundaries of protein domains is an important and challenging task in both experimental and computational structural biology. In this paper, a promising method for detecting the domain structure of a protein from sequence information alone is presented. The method is based on analyzing multiple sequence alignments derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence. Then they are combined into a single predictor using support vector machine. What is more important, the domain detection is first taken as an imbalanted data learning problem. A novel undersampling method is proposed on distance-based maximal entropy in the feature space of Support Vector Machine (SVM). The overall precision is about 80%. Simulation results demonstrate that the method can help not only in predicting the complete 3D structure of a protein but also in the machine learning system on general imbalanced datasets. |
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Keywords: | protein domain boundary SVM imbalanced data learning distance-based maximal entropy |
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