MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources.

Journal: Journal of translational medicine
Published Date: Aug 8, 2019

Abstract

BACKGROUND: Emerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro experiments, more and more attention has been paid to the development of efficient and feasible computational methods to predict the potential associations between miRNA and disease.

Authors

  • Kai Zheng
    University of California, Irvine, Irvine, CA, USA.
  • Zhu-Hong You
    Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China. zhuhongyou@ms.xjb.ac.cn.
  • Lei Wang
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Yong Zhou
    National Institutes for Food and Drug Control, Beijing, 100050, China.
  • Li-Ping Li
    Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
  • Zheng-Wei Li
    School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China.

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