Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network

Abstract Emerging evidence has proved that circular RNAs (circRNAs) are implicated in pathogenic processes. They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA–disease a...

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Veröffentlicht in:Briefings in bioinformatics 2023-01, Vol.24 (1)
Hauptverfasser: Lu, Chengqian, Zhang, Lishen, Zeng, Min, Lan, Wei, Duan, Guihua, Wang, Jianxin
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Zhang, Lishen
Zeng, Min
Lan, Wei
Duan, Guihua
Wang, Jianxin
description Abstract Emerging evidence has proved that circular RNAs (circRNAs) are implicated in pathogenic processes. They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA–disease associations at a lower cost. Aggregating multi-source pathogenesis data helps to alleviate data sparsity and infer potential associations at the system level. The majority of computational approaches construct a homologous network using multi-source data, but they lose the heterogeneity of the data. Effective methods that use the features of multi-source data are considered as a matter of urgency. In this paper, we propose a model (CDHGNN) based on edge-weighted graph attention and heterogeneous graph neural networks for potential circRNA–disease association prediction. The circRNA network, micro RNA network, disease network and heterogeneous network are constructed based on multi-source data. To reflect association probabilities between nodes, an edge-weighted graph attention network model is designed for node features. To assign attention weights to different types of edges and learn contextual meta-path, CDHGNN infers potential circRNA–disease association based on heterogeneous neural networks. CDHGNN outperforms state-of-the-art algorithms in terms of accuracy. Edge-weighted graph attention networks and heterogeneous graph networks have both improved performance significantly. Furthermore, case studies suggest that CDHGNN is capable of identifying specific molecular associations and investigating biomolecular regulatory relationships in pathogenesis. The code of CDHGNN is freely available at https://github.com/BioinformaticsCSU/CDHGNN.
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They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA–disease associations at a lower cost. Aggregating multi-source pathogenesis data helps to alleviate data sparsity and infer potential associations at the system level. The majority of computational approaches construct a homologous network using multi-source data, but they lose the heterogeneity of the data. Effective methods that use the features of multi-source data are considered as a matter of urgency. In this paper, we propose a model (CDHGNN) based on edge-weighted graph attention and heterogeneous graph neural networks for potential circRNA–disease association prediction. The circRNA network, micro RNA network, disease network and heterogeneous network are constructed based on multi-source data. To reflect association probabilities between nodes, an edge-weighted graph attention network model is designed for node features. To assign attention weights to different types of edges and learn contextual meta-path, CDHGNN infers potential circRNA–disease association based on heterogeneous neural networks. CDHGNN outperforms state-of-the-art algorithms in terms of accuracy. Edge-weighted graph attention networks and heterogeneous graph networks have both improved performance significantly. Furthermore, case studies suggest that CDHGNN is capable of identifying specific molecular associations and investigating biomolecular regulatory relationships in pathogenesis. 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For Permissions, please email: journals.permissions@oup.com</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c348t-44a841c7defa8d16d4b46df0893fbc9eaa21442fcc543611c67e2c75811c7f733</citedby><cites>FETCH-LOGICAL-c348t-44a841c7defa8d16d4b46df0893fbc9eaa21442fcc543611c67e2c75811c7f733</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,1604,27924,27925</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/bib/bbac549$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36572658$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lu, Chengqian</creatorcontrib><creatorcontrib>Zhang, Lishen</creatorcontrib><creatorcontrib>Zeng, Min</creatorcontrib><creatorcontrib>Lan, Wei</creatorcontrib><creatorcontrib>Duan, Guihua</creatorcontrib><creatorcontrib>Wang, Jianxin</creatorcontrib><title>Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network</title><title>Briefings in bioinformatics</title><addtitle>Brief Bioinform</addtitle><description>Abstract Emerging evidence has proved that circular RNAs (circRNAs) are implicated in pathogenic processes. They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA–disease associations at a lower cost. Aggregating multi-source pathogenesis data helps to alleviate data sparsity and infer potential associations at the system level. The majority of computational approaches construct a homologous network using multi-source data, but they lose the heterogeneity of the data. Effective methods that use the features of multi-source data are considered as a matter of urgency. In this paper, we propose a model (CDHGNN) based on edge-weighted graph attention and heterogeneous graph neural networks for potential circRNA–disease association prediction. The circRNA network, micro RNA network, disease network and heterogeneous network are constructed based on multi-source data. To reflect association probabilities between nodes, an edge-weighted graph attention network model is designed for node features. To assign attention weights to different types of edges and learn contextual meta-path, CDHGNN infers potential circRNA–disease association based on heterogeneous neural networks. CDHGNN outperforms state-of-the-art algorithms in terms of accuracy. Edge-weighted graph attention networks and heterogeneous graph networks have both improved performance significantly. Furthermore, case studies suggest that CDHGNN is capable of identifying specific molecular associations and investigating biomolecular regulatory relationships in pathogenesis. The code of CDHGNN is freely available at https://github.com/BioinformaticsCSU/CDHGNN.</description><subject>Algorithms</subject><subject>Associations</subject><subject>Biomarkers</subject><subject>Circular RNA</subject><subject>Closed loops</subject><subject>Computational Biology - methods</subject><subject>Computer applications</subject><subject>Graph neural networks</subject><subject>Graph theory</subject><subject>Heterogeneity</subject><subject>MicroRNAs - genetics</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Pathogenesis</subject><subject>RNA, Circular - genetics</subject><issn>1467-5463</issn><issn>1477-4054</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc1LxDAQxYMouq6evEtAEEGqSZMm2aOIHwuiIHouSTrtRttmTVrE_97orh48eHrv8JvHzDyEDig5o2TGzo0z58ZoW_DZBppQLmXGScE3v7yQWcEF20G7Mb4QkhOp6DbaYaKQuSjUBHXzvoYQXN_gykXQETIdo7dOD1Bh64J9vL-I2HzgbmwHl0U_BgtYN02ARg_O99ikoQons4ABgm-gBz9G3AS9XOAexqDbJMO7D697aKvWbYT9tU7R8_XV0-VtdvdwM7-8uMss42rIONeKUysrqLWqqKi44aKqiZqx2tgZaJ1TzvPappuZoNQKCbmVhUpW1pKxKTpZ5S6DfxshDmXnooW21d-7lXliC6k4Uwk9-oO-pBP7tF3JKGVcUElJok5XlA0-xgB1uQyu0-GjpKT8aqFMLZTrFhJ9uM4cTQfVL_vz9gQcrwA_Lv9N-gQNm5Hm</recordid><startdate>20230119</startdate><enddate>20230119</enddate><creator>Lu, Chengqian</creator><creator>Zhang, Lishen</creator><creator>Zeng, Min</creator><creator>Lan, Wei</creator><creator>Duan, Guihua</creator><creator>Wang, Jianxin</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7SC</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>K9.</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20230119</creationdate><title>Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network</title><author>Lu, Chengqian ; 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subjects Algorithms
Associations
Biomarkers
Circular RNA
Closed loops
Computational Biology - methods
Computer applications
Graph neural networks
Graph theory
Heterogeneity
MicroRNAs - genetics
Neural networks
Neural Networks, Computer
Pathogenesis
RNA, Circular - genetics
title Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network
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