{"title":"Multirelational Hypergraph Representation Learning for Predicting circRNA-miRNA Associations","authors":"Wenjing Yin, Shudong Wang, Yuanyuan Zhang, Sibo Qiao, Wenhao Wu, Hengxiao Li","doi":"10.1021/acs.jcim.4c01436","DOIUrl":null,"url":null,"abstract":"One of the principal functions of circular RNA (circRNA) is to participate in gene regulation by sponging microRNAs (miRNAs). Using accumulated circRNA-miRNA associations (CMAs) to construct computational models for predicting potential associations provides a crucial tool for accelerating the validation of reliable associations through traditional experiments. Nevertheless, the current prediction models are constrained in their capacity to represent the higher-order relationships of CMAs and thus require further enhancement in terms of their predictive efficacy. In order to address this issue, we propose a new model based on multirelational hypergraph representation learning (MRHRL). This model employs hypergraphs to capture various higher-order relationships among RNAs and aggregates complementary information through a view attention mechanism. Furthermore, MRHRL introduces a hyperedge-level reconstruction task, jointly optimizing the prediction and reconstruction tasks within a unified framework to uncover potential information, thereby enhancing the model’s predictive and generalization capabilities. Experiments conducted on three real-world data sets demonstrate that MRHRL achieves satisfactory results in CMAs prediction, significantly outperforming existing prediction models.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Information and Modeling ","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.jcim.4c01436","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0
Abstract
One of the principal functions of circular RNA (circRNA) is to participate in gene regulation by sponging microRNAs (miRNAs). Using accumulated circRNA-miRNA associations (CMAs) to construct computational models for predicting potential associations provides a crucial tool for accelerating the validation of reliable associations through traditional experiments. Nevertheless, the current prediction models are constrained in their capacity to represent the higher-order relationships of CMAs and thus require further enhancement in terms of their predictive efficacy. In order to address this issue, we propose a new model based on multirelational hypergraph representation learning (MRHRL). This model employs hypergraphs to capture various higher-order relationships among RNAs and aggregates complementary information through a view attention mechanism. Furthermore, MRHRL introduces a hyperedge-level reconstruction task, jointly optimizing the prediction and reconstruction tasks within a unified framework to uncover potential information, thereby enhancing the model’s predictive and generalization capabilities. Experiments conducted on three real-world data sets demonstrate that MRHRL achieves satisfactory results in CMAs prediction, significantly outperforming existing prediction models.
期刊介绍:
The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery.
Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field.
As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.