{"title":"在异构生物网络上进行药物重新定位的调控感知图学习","authors":"","doi":"10.1016/j.ins.2024.121360","DOIUrl":null,"url":null,"abstract":"<div><p>Drug repositioning (DR) is crucial for identifying new disease indications for existing drugs and enhancing their clinical utility. Despite the effectiveness of various artificial intelligence techniques in discovering novel drug-disease associations (DDAs), many algorithms primarily focus on incorporating biological knowledge of drugs and diseases into DDA networks, often overlooking the rich connectivity patterns inherent in heterogeneous biological networks. In this study, we leveraged diverse connectivity patterns to gain new insights into the regulatory mechanisms of drugs acting on target proteins in diseases. We defined a set of meta-paths to reveal different regulatory mechanisms, each corresponding to distinct connectivity patterns. For each meta-path, we constructed a regulation graph through random-walk sampling of its instances in the network and obtained drug and disease embeddings through regulation-aware graph representation learning. Subsequently, we proposed a novel multi-view attention mechanism to enhance drug and disease representations. The task of predicting DDAs was accomplished using the XGBoost classifier based on the final representations of drugs and diseases. The experimental results demonstrated the superior performance of our method, RGLDR, on three benchmark datasets under ten-fold cross-validation, outperforming state-of-the-art DR algorithms across several evaluation metrics. Furthermore, case studies on two diseases indicated that RGLDR is a promising DR tool that leverages meaningful connectivity patterns for improved efficacy.</p></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Regulation-aware graph learning for drug repositioning over heterogeneous biological network\",\"authors\":\"\",\"doi\":\"10.1016/j.ins.2024.121360\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Drug repositioning (DR) is crucial for identifying new disease indications for existing drugs and enhancing their clinical utility. Despite the effectiveness of various artificial intelligence techniques in discovering novel drug-disease associations (DDAs), many algorithms primarily focus on incorporating biological knowledge of drugs and diseases into DDA networks, often overlooking the rich connectivity patterns inherent in heterogeneous biological networks. In this study, we leveraged diverse connectivity patterns to gain new insights into the regulatory mechanisms of drugs acting on target proteins in diseases. We defined a set of meta-paths to reveal different regulatory mechanisms, each corresponding to distinct connectivity patterns. For each meta-path, we constructed a regulation graph through random-walk sampling of its instances in the network and obtained drug and disease embeddings through regulation-aware graph representation learning. Subsequently, we proposed a novel multi-view attention mechanism to enhance drug and disease representations. The task of predicting DDAs was accomplished using the XGBoost classifier based on the final representations of drugs and diseases. The experimental results demonstrated the superior performance of our method, RGLDR, on three benchmark datasets under ten-fold cross-validation, outperforming state-of-the-art DR algorithms across several evaluation metrics. Furthermore, case studies on two diseases indicated that RGLDR is a promising DR tool that leverages meaningful connectivity patterns for improved efficacy.</p></div>\",\"PeriodicalId\":51063,\"journal\":{\"name\":\"Information Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2024-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Sciences\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S002002552401274X\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"N/A\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S002002552401274X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"N/A","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
药物重新定位(DR)对于确定现有药物的新疾病适应症和提高其临床效用至关重要。尽管各种人工智能技术在发现新的药物-疾病关联(DDA)方面效果显著,但许多算法主要侧重于将药物和疾病的生物学知识纳入 DDA 网络,往往忽略了异构生物网络中固有的丰富连接模式。在本研究中,我们利用不同的连接模式,对药物作用于疾病靶蛋白的调控机制获得了新的认识。我们定义了一组元路径来揭示不同的调控机制,每种元路径都与不同的连接模式相对应。对于每个元路径,我们通过对其网络中的实例进行随机漫步采样来构建调控图,并通过调控感知图表示学习来获得药物和疾病嵌入。随后,我们提出了一种新颖的多视角关注机制来增强药物和疾病表征。根据最终的药物和疾病表征,使用 XGBoost 分类器完成了预测 DDA 的任务。实验结果表明,在十倍交叉验证下,我们的方法 RGLDR 在三个基准数据集上表现出色,在多个评价指标上都优于最先进的 DR 算法。此外,对两种疾病的案例研究表明,RGLDR 是一种很有前途的 DR 工具,它能利用有意义的连接模式提高疗效。
Regulation-aware graph learning for drug repositioning over heterogeneous biological network
Drug repositioning (DR) is crucial for identifying new disease indications for existing drugs and enhancing their clinical utility. Despite the effectiveness of various artificial intelligence techniques in discovering novel drug-disease associations (DDAs), many algorithms primarily focus on incorporating biological knowledge of drugs and diseases into DDA networks, often overlooking the rich connectivity patterns inherent in heterogeneous biological networks. In this study, we leveraged diverse connectivity patterns to gain new insights into the regulatory mechanisms of drugs acting on target proteins in diseases. We defined a set of meta-paths to reveal different regulatory mechanisms, each corresponding to distinct connectivity patterns. For each meta-path, we constructed a regulation graph through random-walk sampling of its instances in the network and obtained drug and disease embeddings through regulation-aware graph representation learning. Subsequently, we proposed a novel multi-view attention mechanism to enhance drug and disease representations. The task of predicting DDAs was accomplished using the XGBoost classifier based on the final representations of drugs and diseases. The experimental results demonstrated the superior performance of our method, RGLDR, on three benchmark datasets under ten-fold cross-validation, outperforming state-of-the-art DR algorithms across several evaluation metrics. Furthermore, case studies on two diseases indicated that RGLDR is a promising DR tool that leverages meaningful connectivity patterns for improved efficacy.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.