Query-Adaptive Ranking with Support Vector Machines for Protein Homology Prediction

  • Yan Fu
  • Rong Pan
  • Qiang Yang
  • Wen Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6674)


Protein homology prediction is a crucial step in template-based protein structure prediction. The functions that rank the proteins in a database according to their homologies to a query protein is the key to the success of protein structure prediction. In terms of information retrieval, such functions are called ranking functions, and are often constructed by machine learning approaches. Different from traditional machine learning problems, the feature vectors in the ranking-function learning problem are not identically and independently distributed, since they are calculated with regard to queries and may vary greatly in statistical characteristics from query to query. At present, few existing algorithms make use of the query-dependence to improve ranking performance. This paper proposes a query-adaptive ranking-function learning algorithm for protein homology prediction. Experiments with the support vector machine (SVM) used as the benchmark learner demonstrate that the proposed algorithm can significantly improve the ranking performance of SVMs in the protein homology prediction task.


Protein homology prediction information retrieval ranking function machine learning support vector machine 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yan Fu
    • 1
  • Rong Pan
    • 2
  • Qiang Yang
    • 3
  • Wen Gao
    • 4
  1. 1.Institute of Computing Technology and Key Lab of Intelligent Information ProcessingChinese Academy of SciencesBeijingChina
  2. 2.School of Information Science and TechnologySun Yat-sen UniversityGuangzhouChina
  3. 3.Department of Computer Science and EngineeringHong Kong University of Science and TechnologyHong Kong, China
  4. 4.Institute of Digital MediaPeking UniversityBeijingChina

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