MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network
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 associ...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Pharmacology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2021.799108/full |
| _version_ | 1798022418943442944 |
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| author | Yanqing Niu Congzhi Song Yuchong Gong Wen Zhang |
| author_facet | Yanqing Niu Congzhi Song Yuchong Gong Wen Zhang |
| author_sort | Yanqing Niu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T17:29:36Z |
| format | Article |
| id | doaj.art-0a86b2280a9b450298981b6ed54c1fcc |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-04-11T17:29:36Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-0a86b2280a9b450298981b6ed54c1fcc2022-12-22T04:12:09ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122022-01-011210.3389/fphar.2021.799108799108MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional NetworkYanqing Niu0Congzhi Song1Yuchong Gong2Wen Zhang3School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan, ChinaSchool of Computer Science, Wuhan University, Wuhan, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan, ChinaMiRNAs 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.https://www.frontiersin.org/articles/10.3389/fphar.2021.799108/fullmiRNA-drug resistance associationgraph convolutional networkmultimodaldeep learningattention neural network |
| spellingShingle | Yanqing Niu Congzhi Song Yuchong Gong Wen Zhang MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network Frontiers in Pharmacology miRNA-drug resistance association graph convolutional network multimodal deep learning attention neural network |
| title | MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network |
| title_full | MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network |
| title_fullStr | MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network |
| title_full_unstemmed | MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network |
| title_short | MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network |
| title_sort | mirna drug resistance association prediction through the attentive multimodal graph convolutional network |
| topic | miRNA-drug resistance association graph convolutional network multimodal deep learning attention neural network |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2021.799108/full |
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