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

• Main Authors: Yanqing Niu (Author), Congzhi Song (Author), Yuchong Gong (Author), Wen Zhang (Author)
• Format: Book
• Published: Frontiers Media S.A., 2022-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

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.

Item Description

1663-9812
10.3389/fphar.2021.799108