ArticleFrontiers in pharmacology2021
MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network.
Article in Frontiers in pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MDCL: Multi-source Information Fusion and Collaborative Contrastive Learning for Inferring miRNA-Drug Sensitivity and Resistance Associations.Interdisciplinary sciences, computational life sciences · 2026Article
- MIFNDRA: an innovative knowledge-enhanced multimodal fusion and graph learning framework for predicting drug resistance-related ncRNAs.Briefings in bioinformatics · 2026Article
- DMGAT: predicting ncRNA-drug resistance associations based on diffusion map and heterogeneous graph attention network.Briefings in bioinformatics · 2025Article
- Empowering Graph Neural Networks with Block-Based Dual Adaptive Deep Adjustment for Drug Resistance-Related NcRNA Discovery.Journal of chemical information and modeling · 2024Article
- DNA and RNA Molecules as a Foundation of Therapy Strategies for Treatment of Cardiovascular Diseases.Pharmaceutics · 2023Review
- Using BERT to identify drug-target interactions from whole PubMed.BMC bioinformatics · 2022Article
- An Integrative Heterogeneous Graph Neural Network-Based Method for Multi-Labeled Drug Repurposing.Frontiers in pharmacology · 2022Article
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4 authors.
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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.
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