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Journal of Bionic Engineering

, Volume 5, Issue 3, pp 215–223 | Cite as

A Novel Method for Prediction of Protein Domain Using Distance-Based Maximal Entropy

  • Shu-xue Zou
  • Yan-xin Huang
  • Yan Wang
  • Chun-guang ZhoEmail author
Article

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 imbalanced 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.

Keywords

protein domain boundary SVM imbalanced data learning distance-based maximal entropy 

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Copyright information

© Jilin University 2008

Authors and Affiliations

  • Shu-xue Zou
    • 1
  • Yan-xin Huang
    • 1
  • Yan Wang
    • 1
  • Chun-guang Zho
    • 1
    Email author
  1. 1.Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and TechnologyJilin UniversityChangchunP. R. China

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