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• W4206822016 abstract "MicroRNA (miRNA) has became an increasingly important class of attractive drug targets in recent studies. However, there are only few computational tools aiming to predict drugmi-RNA resistance associations. Hence, it is of great significance to develop effective and high accuracy methods for predicting drugmi-RNA resistance associations. In this work, we propose a novel method abbreviated as “DMR-GCN”, which enhances drugmi-RNA resistance interaction prediction by using layer attention graph convolution network and multi channel feature extraction. Specifically, DMR-GCN first constructs a heterogeneous network based on known drug-miRNA interactions, drug-drug similarities and miRNA-miRNA similarities. Secondly, layer attention graph convolution network is used to extract drug representations from the drug molecular graph and the heterogeneous network. We concatenate the extracted representations from molecular graph and heterogeneous network as the drug embedding vectors. Similarly, miRNA representations extracted from the heterogeneous network and the miRNA expression features embedded by MLP are concatenated as the miRNA embedding vectors. Further, we utilize Multi-Layer Perceptron (MLP), Generalized Tensor Factorization (GTF) and Compressed Tensor Network (CTN) to extract node-pair representations from different aspects. Finally, the predictive scores for unobserved drug-miRNA resistance associations are given by a fully connection layer with the integrated embeddings. In the evaluation experiments, DMR-GCN achieves an area under the precision-recall curve of 0.2920 and an area under the receiver-operating characteristic curve of 0.9433, which are better than the state-of-the-art prediction methods. The experimental results demonstrate that layer attention mechanism produces satisfying results for learning representations from graph, and integrating multi channel feature extraction can make further improvements. In conclusion, DMR-GCN is a promising method for predicting drug-miRNA resistance associations." @default.
• W4206822016 created "2022-01-25" @default.
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• W4206822016 date "2021-12-09" @default.
• W4206822016 modified "2023-09-27" @default.
• W4206822016 title "Predicting Drug-miRNA Resistance with Layer Attention Graph Convolution Network and Multi Channel Feature Extraction" @default.
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• W4206822016 doi "https://doi.org/10.1109/bibm52615.2021.9669497" @default.
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