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Predicting Microbe-Disease Association by Kernelized Bayesian Matrix Factorization

  • Sisi Chen
  • Dan Liu
  • Jia Zheng
  • Pingtao Chen
  • Xiaohua Hu
  • Xingpeng Jiang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

The study of microbe-disease associations can be utilized as a valuable material for understanding disease pathogenesis. Developing a highly accurate algorithm model for predicting disease-related microbes will provide a basis for targeted treatment of the disease. In this paper, we propose an approach based on Kernelized Bayesian Matrix Factorization (KBMF) to predict microbe-disease association, based on the Gaussian interaction profile kernel similarity for microbes and diseases. The prediction performance of the method was evaluated by five-fold cross validation. KBMF achieved reliable results which is better than several state-of-the-art methods with around 8% improvement of AUC. Furthermore, case studies have demonstrated the reliability of the method.

Keywords

Microbe Matrix factorization Bayesian Biological network 

Notes

Acknowledgement

This research is supported by the National Natural Science Foundation of China (No. 61532008), the Excellent Doctoral Breeding Project of CCNU, the Self-determined Research Funds of CCNU from the Colleges’ Basic Research and Operation of MOE (No. CCNU16KFY04).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sisi Chen
    • 1
  • Dan Liu
    • 1
  • Jia Zheng
    • 1
  • Pingtao Chen
    • 2
  • Xiaohua Hu
    • 1
    • 3
  • Xingpeng Jiang
    • 1
  1. 1.School of ComputerCentral China Normal UniversityWuhanChina
  2. 2.School of Physical SciencesUniversity of Science and Technology of ChinaHefeiChina
  3. 3.College of Computing and InformaticsDrexel UniversityPhiladelphiaUSA

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