iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network.

Journal: PLoS computational biology
Published Date:

Abstract

MOTIVATION: Piwi-interacting RNAs (piRNAs) play a critical role in the progression of various diseases. Accurately identifying the associations between piRNAs and diseases is important for diagnosing and prognosticating diseases. Although some computational methods have been proposed to detect piRNA-disease associations, it is challenging for these methods to effectively capture nonlinear and complex relationships between piRNAs and diseases because of the limited training data and insufficient association representation.

Authors

  • Jialu Hou
    School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
  • Hang Wei
    Institute of Quality Standards & Testing Technology for Agro-products, Fujian Academy of Agricultural Sciences/ Fujian Key Laboratory of Agro-products Quality and Safety, Fuzhou, 350003, China.
  • Bin Liu
    Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Endocrinology, Neijiang First People's Hospital, Chongqing, China.