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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creator | Lu, Chengqian 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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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><identifier>ISSN: 1467-5463</identifier><identifier>EISSN: 1477-4054</identifier><identifier>DOI: 10.1093/bib/bbac549</identifier><identifier>PMID: 36572658</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>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</subject><ispartof>Briefings in bioinformatics, 2023-01, Vol.24 (1)</ispartof><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2022</rights><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.</rights><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. 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 ; Zhang, Lishen ; Zeng, Min ; Lan, Wei ; Duan, Guihua ; Wang, Jianxin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c348t-44a841c7defa8d16d4b46df0893fbc9eaa21442fcc543611c67e2c75811c7f733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Associations</topic><topic>Biomarkers</topic><topic>Circular RNA</topic><topic>Closed loops</topic><topic>Computational Biology - methods</topic><topic>Computer applications</topic><topic>Graph neural networks</topic><topic>Graph theory</topic><topic>Heterogeneity</topic><topic>MicroRNAs - genetics</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Pathogenesis</topic><topic>RNA, Circular - genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lu, Chengqian</creatorcontrib><creatorcontrib>Zhang, Lishen</creatorcontrib><creatorcontrib>Zeng, Min</creatorcontrib><creatorcontrib>Lan, Wei</creatorcontrib><creatorcontrib>Duan, Guihua</creatorcontrib><creatorcontrib>Wang, Jianxin</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Briefings in bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lu, Chengqian</au><au>Zhang, Lishen</au><au>Zeng, Min</au><au>Lan, Wei</au><au>Duan, Guihua</au><au>Wang, Jianxin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network</atitle><jtitle>Briefings in bioinformatics</jtitle><addtitle>Brief Bioinform</addtitle><date>2023-01-19</date><risdate>2023</risdate><volume>24</volume><issue>1</issue><issn>1467-5463</issn><eissn>1477-4054</eissn><abstract>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.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>36572658</pmid><doi>10.1093/bib/bbac549</doi></addata></record> |
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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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