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DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank

  • Jieyao Deng
  • Qingjun Yuan
  • Hiroshi Mamitsuka
  • Shanfeng Zhu
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1807)

Abstract

Identifying drug-target interactions is crucial for the success of drug discovery. Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. By utilizing the “Learning to rank” framework, we propose a new method, DrugE-Rank, to combine these two different types of methods for improving the prediction performance of new candidate drugs and targets. DrugE-Rank is available at http://datamining-iip.fudan.edu.cn/service/DrugE-Rank/.

Key words

DrugE-Rank Learning to rank Drug discovery 

Notes

Acknowledgments

This work has been partially supported by National Natural Science Foundation of China (Grant Nos: 61572139), MEXT KAKENHI #16H02868, and FiDiPro by Tekes.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jieyao Deng
    • 1
    • 2
  • Qingjun Yuan
    • 1
    • 2
  • Hiroshi Mamitsuka
    • 3
    • 4
  • Shanfeng Zhu
    • 1
    • 2
    • 5
  1. 1.School of Computer ScienceFudan UniversityShanghaiChina
  2. 2.Shanghai Key Lab of Intelligent Information ProcessingFudan UniversityShanghaiChina
  3. 3.Bioinformatics Center, Institute for Chemical ResearchKyoto UniversityUjiJapan
  4. 4.Department of Computer ScienceAalto UniversityEspooFinland
  5. 5.Center for Computational System BiologyFudan UniversityShanghaiChina

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