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Using the Ranking-Based KNN Approach for Drug Repositioning Based on Multiple Information

  • Xin Tian
  • Mingyuan Xin
  • Jian Luo
  • Zhenran JiangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)

Abstract

Using effective computer methods to infer potential drug-disease relationships can provide clues for the discovery new uses of old drugs. This paper introduced a Ranking-based k-Nearest Neighbor (Re-KNN) method to drug repositioning for cardiovascular diseases. The main characteristic of the Re-KNN lies in combining conventional KNN algorithm with Ranking SVM (Support Vector Machine) algorithm to get neighbors that are more trustable. By integrating the chemical structural similarity, target-based similarity, side-effect similarity and topological similarity information, Re-KNN method can obtain an improved AUC (Area under ROC Curve) and AUPR (Area under Precision-Recall curve) compared with other methods, which prove the validity and efficiency of multiple features integration.

Keywords

Cardiovascular disease Drug reposition Ranking-based KNN Ranking SVM 

Notes

Acknowledgements

This work was partially supported by National Basic Research Program of China (Grants No. 2012CB910400), the Fundamental Research Funds for the Central Universities (78260026), and National Science and Technology Support Plan Project (2015BAH12F01).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xin Tian
    • 1
  • Mingyuan Xin
    • 1
  • Jian Luo
    • 1
  • Zhenran Jiang
    • 2
    Email author
  1. 1.Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina

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