Scoring disease-microRNA associations by integrating disease hierarchy into graph convolutional networks

作者：

Highlights：

• Inferring disease-associated miRNAs using graph convolutional network under an interaction network.

• The newly presented method is superior to state-of-the-art methods for predicting unseen diseasemiRNA associations.

• Ablation analysis demonstrates that introducing disease hierarchy into model training improves prediction performance.

• Case studies on three diseases shows our proposed methods can identify verified associated miRNAs.

摘要

•Inferring disease-associated miRNAs using graph convolutional network under an interaction network.•The newly presented method is superior to state-of-the-art methods for predicting unseen diseasemiRNA associations.•Ablation analysis demonstrates that introducing disease hierarchy into model training improves prediction performance.•Case studies on three diseases shows our proposed methods can identify verified associated miRNAs.

论文关键词：microRNAs,Protein coding genes,Interaction network,Graph convolutional network,Disease hierarchy

论文评审过程：Received 15 November 2019, Revised 26 March 2020, Accepted 15 April 2020, Available online 3 May 2020, Version of Record 5 June 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107385