Chenyue Lei, Xiujuan Lei, Lian Liu, Jianrui Chen, Fang-Xiang Wu
{"title":"基于异构网络多源特征融合的mirna -药物相互作用预测。","authors":"Chenyue Lei, Xiujuan Lei, Lian Liu, Jianrui Chen, Fang-Xiang Wu","doi":"10.1007/s12539-025-00775-7","DOIUrl":null,"url":null,"abstract":"<p><p>Resistance to treatment remains one of the greatest challenges in cancer therapy. Recent studies have shown that drug sensitivity is closely associated with miRNA expression, highlighting the importance of predicting miRNA-drug interactions (MDIs) in understanding drug resistance mechanisms. Within this study, we propose an innovative method named MSFFMDI, which employs a dual-channel multi-source feature fusion framework based on heterogeneous networks to predict potential MDIs. The first channel focuses on attribute feature extraction. For miRNAs, we integrate the k-mer algorithm with word2vec to transform sequences into low-dimensional embeddings that capture semantic and structural information. For drugs, we utilize the graph isomorphism network to learn molecular structure features, and apply mol2vec to capture chemical and functional sequence features. The second channel extracts topological features by constructing a heterogeneous network based on integrated similarities and known associations between miRNAs and drugs. A graph attention network is used to update node embeddings, and a multi-scale convolutional neural network is employed to further extract topological representations. The features from both channels are fused and reduced via principal component analysis before being used for final prediction. A large number of rich experimental results show that MSFFMDI demonstrates excellent predictive performance on two datasets. Case studies further validate its robust performance. Overall, MSFFMDI provides a powerful and interpretable framework for predicting MDIs and offers potential insights into the mechanisms of drug resistance.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting miRNA-Drug Interactions Based on Multi-source Feature Fusion of Heterogeneous Network.\",\"authors\":\"Chenyue Lei, Xiujuan Lei, Lian Liu, Jianrui Chen, Fang-Xiang Wu\",\"doi\":\"10.1007/s12539-025-00775-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Resistance to treatment remains one of the greatest challenges in cancer therapy. Recent studies have shown that drug sensitivity is closely associated with miRNA expression, highlighting the importance of predicting miRNA-drug interactions (MDIs) in understanding drug resistance mechanisms. Within this study, we propose an innovative method named MSFFMDI, which employs a dual-channel multi-source feature fusion framework based on heterogeneous networks to predict potential MDIs. The first channel focuses on attribute feature extraction. For miRNAs, we integrate the k-mer algorithm with word2vec to transform sequences into low-dimensional embeddings that capture semantic and structural information. For drugs, we utilize the graph isomorphism network to learn molecular structure features, and apply mol2vec to capture chemical and functional sequence features. The second channel extracts topological features by constructing a heterogeneous network based on integrated similarities and known associations between miRNAs and drugs. A graph attention network is used to update node embeddings, and a multi-scale convolutional neural network is employed to further extract topological representations. The features from both channels are fused and reduced via principal component analysis before being used for final prediction. A large number of rich experimental results show that MSFFMDI demonstrates excellent predictive performance on two datasets. Case studies further validate its robust performance. 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Predicting miRNA-Drug Interactions Based on Multi-source Feature Fusion of Heterogeneous Network.
Resistance to treatment remains one of the greatest challenges in cancer therapy. Recent studies have shown that drug sensitivity is closely associated with miRNA expression, highlighting the importance of predicting miRNA-drug interactions (MDIs) in understanding drug resistance mechanisms. Within this study, we propose an innovative method named MSFFMDI, which employs a dual-channel multi-source feature fusion framework based on heterogeneous networks to predict potential MDIs. The first channel focuses on attribute feature extraction. For miRNAs, we integrate the k-mer algorithm with word2vec to transform sequences into low-dimensional embeddings that capture semantic and structural information. For drugs, we utilize the graph isomorphism network to learn molecular structure features, and apply mol2vec to capture chemical and functional sequence features. The second channel extracts topological features by constructing a heterogeneous network based on integrated similarities and known associations between miRNAs and drugs. A graph attention network is used to update node embeddings, and a multi-scale convolutional neural network is employed to further extract topological representations. The features from both channels are fused and reduced via principal component analysis before being used for final prediction. A large number of rich experimental results show that MSFFMDI demonstrates excellent predictive performance on two datasets. Case studies further validate its robust performance. Overall, MSFFMDI provides a powerful and interpretable framework for predicting MDIs and offers potential insights into the mechanisms of drug resistance.
期刊介绍:
Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology.
The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer.
The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.