MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network

Yanqing Niu; Congzhi Song; Yuchong Gong; Wen Zhang

doi:10.3389/fphar.2021.799108

MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network

Front Pharmacol. 2022 Jan 12:12:799108. doi: 10.3389/fphar.2021.799108. eCollection 2021.

Authors

Yanqing Niu¹, Congzhi Song², Yuchong Gong³, Wen Zhang²

Affiliations

¹ School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan, China.
² College of Informatics, Huazhong Agricultural University, Wuhan, China.
³ School of Computer Science, Wuhan University, Wuhan, China.

Abstract

MiRNAs can regulate genes encoding specific proteins which are related to the efficacy of drugs, and predicting miRNA-drug resistance associations is of great importance. In this work, we propose an attentive multimodal graph convolution network method (AMMGC) to predict miRNA-drug resistance associations. AMMGC learns the latent representations of drugs and miRNAs from four graph convolution sub-networks with distinctive combinations of features. Then, an attention neural network is employed to obtain attentive representations of drugs and miRNAs, and miRNA-drug resistance associations are predicted by the inner product of learned attentive representations. The computational experiments show that AMMGC outperforms other state-of-the-art methods and baseline methods, achieving the AUPR score of 0.2399 and the AUC score of 0.9467. The analysis demonstrates that leveraging multiple features of drugs and miRNAs can make a contribution to the miRNA-drug resistance association prediction. The usefulness of AMMGC is further validated by case studies.

Keywords: attention neural network; deep learning; graph convolutional network; miRNA-drug resistance association; multimodal.