MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes.

Journal: Interdisciplinary sciences, computational life sciences
Published Date: Apr 15, 2022

Abstract

MOTIVATION: Exploring the interrelationships between microbes and disease can help microbiologists make decisions and plan treatments. Predicting new microbe-disease associations currently relies on biological experiments and domain knowledge, which is time-consuming and inefficient. Automated algorithms are used to uncover the intrinsic link between microbes and disease. However, due to data noise and inadequate understanding of relevant biology, the efficient prediction of microbe-disease associations is still crucial. This study develops a multi-view graph augmentation convolutional network (MVGCNMDA) to predict potential disease-associated microbes.

Authors

  • Meifang Hua
    School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China.
  • Shengpeng Yu
    School of Information Science and Engineering, Shandong Normal University, Jinan, China.
  • Tianyu Liu
    Department of Automation, Tsinghua University,Beijing, China.
  • Xue Yang
    Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
  • Hong Wang
    Department of Cardiology, Liuzhou Workers' Hospital, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.

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