{"title":"DTRE: A model for predicting drug-target interactions of endometrial cancer based on heterogeneous graph","authors":"","doi":"10.1016/j.future.2024.07.012","DOIUrl":null,"url":null,"abstract":"<div><p>Endometrial cancer is one of the most common gynecological malignancies affecting women worldwide, posing a serious threat to women’s health. Moreover, the identification of drug-target interactions (DTIs) is typically a time-consuming and costly critical step in drug discovery. In order to identify potential DTIs to enhance targeted therapy for endometrial cancer, we propose a deep learning model named DTRE (Drug-Target Relationship Enhanced) based on a heterogeneous graph to predict DTIs, which utilizes the relationships between drugs and targets to effectively capture their interactions. In the heterogeneous graph, nodes represent drugs and targets, and edges represent their interactions, then the representations of drugs and targets are learned through graph convolutional network, graph attention network and attention mechanism. Experimental results on the dataset proposed in this paper show that the AUC and AUPR of DTRE achieve 0.870 and 0.872 respectively, significantly outperforming comparative models and indicating that DTRE can effectively predict DTIs when applied to large-scale data. Additionally, DTRE also predicts the potential DTIs for endometrial cancer, providing new insights into targeted therapy for it.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167739X24003753/pdfft?md5=ec2c21087b0dc076540861fc166a4572&pid=1-s2.0-S0167739X24003753-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X24003753","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Endometrial cancer is one of the most common gynecological malignancies affecting women worldwide, posing a serious threat to women’s health. Moreover, the identification of drug-target interactions (DTIs) is typically a time-consuming and costly critical step in drug discovery. In order to identify potential DTIs to enhance targeted therapy for endometrial cancer, we propose a deep learning model named DTRE (Drug-Target Relationship Enhanced) based on a heterogeneous graph to predict DTIs, which utilizes the relationships between drugs and targets to effectively capture their interactions. In the heterogeneous graph, nodes represent drugs and targets, and edges represent their interactions, then the representations of drugs and targets are learned through graph convolutional network, graph attention network and attention mechanism. Experimental results on the dataset proposed in this paper show that the AUC and AUPR of DTRE achieve 0.870 and 0.872 respectively, significantly outperforming comparative models and indicating that DTRE can effectively predict DTIs when applied to large-scale data. Additionally, DTRE also predicts the potential DTIs for endometrial cancer, providing new insights into targeted therapy for it.
期刊介绍:
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.