{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:03:24Z","timestamp":1776272604088,"version":"3.50.1"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T00:00:00Z","timestamp":1736726400000},"content-version":"vor","delay-in-days":52,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2022YFC3400400"],"award-info":[{"award-number":["2022YFC3400400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2023YFC3503400"],"award-info":[{"award-number":["2023YFC3503400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSFC-FDCT","award":["62361166662"],"award-info":[{"award-number":["62361166662"]}]},{"name":"Innovative Research Group Project of Hunan Province","award":["2024JJ1002"],"award-info":[{"award-number":["2024JJ1002"]}]},{"name":"Key R&D Program of Hunan Province","award":["2023GK2004"],"award-info":[{"award-number":["2023GK2004"]}]},{"name":"Key R&D Program of Hunan Province","award":["2023SK2059"],"award-info":[{"award-number":["2023SK2059"]}]},{"name":"Key R&D Program of Hunan Province","award":["2023SK2060"],"award-info":[{"award-number":["2023SK2060"]}]},{"name":"Top 10 Technical Key Project in Hunan Province","award":["2023GK1010"],"award-info":[{"award-number":["2023GK1010"]}]},{"name":"Key Technologies R&D Program of Guangdong Province","award":["2023B1111030004"],"award-info":[{"award-number":["2023B1111030004"]}]},{"name":"Funds of State Key Laboratory of Chemo\/Biosensing and Chemometrics"},{"name":"National Supercomputing Center in Changsha, and Peng Cheng Lab"},{"name":"Graduate Research Innovation Project of Hunan Province","award":["QL20230101"],"award-info":[{"award-number":["QL20230101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The role of cell-cell communications (CCCs) is increasingly recognized as being important to differentiation, invasion, metastasis, and drug resistance in tumoral tissues. Developing CCC inference methods using traditional experimental methods are time-consuming, labor-intensive, cannot handle large amounts of data. To facilitate inference of CCCs, we proposed a computational framework, called CellMsg, which involves two primary steps: identifying ligand\u2013receptor interactions (LRIs) and measuring the strength of LRIs-mediated CCCs. Specifically, CellMsg first identifies high-confident LRIs based on multimodal features of ligands and receptors and graph convolutional networks. Then, CellMsg measures the strength of intercellular communication by combining the identified LRIs and single-cell RNA-seq data using a three-point estimation method. Performance evaluation on four benchmark LRI datasets by five-fold cross validation demonstrated that CellMsg accurately captured the relationships between ligands and receptors, resulting in the identification of high-confident LRIs. Compared with other methods of identifying LRIs, CellMsg has better prediction performance and robustness. Furthermore, the LRIs identified by CellMsg were successfully validated through molecular docking. Finally, we examined the overlap of LRIs between CellMsg and five other classical CCC databases, as well as the intercellular crosstalk among seven cell types within a human melanoma tissue. In summary, CellMsg establishes a complete, reliable, and well-organized LRI database and an effective CCC strength evaluation method for each single-cell RNA-seq data. It provides a computational tool allowing researchers to decipher intercellular communications. CellMsg is freely available at https:\/\/github.com\/pengsl-lab\/CellMsg.<\/jats:p>","DOI":"10.1093\/bib\/bbae716","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T03:10:05Z","timestamp":1736737805000},"source":"Crossref","is-referenced-by-count":4,"title":["CellMsg: graph convolutional networks for ligand\u2013receptor-mediated cell-cell communication analysis"],"prefix":"10.1093","volume":"26","author":[{"given":"Hong","family":"Xia","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082 ,","place":["China"]}]},{"given":"Boya","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082 ,","place":["China"]}]},{"given":"Debin","family":"Qiao","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, ZhengZhou University , Zhengzhou 450001 ,","place":["China"]}]},{"given":"Shaoliang","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082 ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,1,12]]},"reference":[{"key":"2025011303095190600_ref1","doi-asserted-by":"publisher","first-page":"1671","DOI":"10.1126\/science.1313187","article-title":"Intercellular communication and cell-cell adhesion","volume":"255","author":"Jonathan Singer","year":"1992","journal-title":"Science"},{"key":"2025011303095190600_ref2","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1007\/s13238-020-00727-5","article-title":"New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data","volume":"11","author":"Shao","year":"2020","journal-title":"Protein&Cell"},{"key":"2025011303095190600_ref3","doi-asserted-by":"publisher","first-page":"4038","DOI":"10.1182\/blood-2006-10-051755","article-title":"Hepcidin antimicrobial peptide transgenic mice exhibit features of the anemia of inflammation","volume":"109","author":"Roy","year":"2007","journal-title":"Blood"},{"key":"2025011303095190600_ref4","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1038\/s42003-018-0137-0","article-title":"Origin of cancer-associated fibroblasts and tumor-associated macrophages in humans after sex-mismatched bone marrow transplantation","volume":"1","author":"Kurashige","year":"2018","journal-title":"Commun Biol"},{"key":"2025011303095190600_ref5","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/JBHI.2023.3333828","article-title":"Celldialog: A computational framework for ligand-receptor-mediated cell-cell communication analysis","volume":"28","author":"Peng","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"2025011303095190600_ref6","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1109\/TNB.2023.3278685","article-title":"CellEnBoost: a boosting-based ligand-receptor interaction identification model for cell-to-cell communication inference","volume":"22","author":"Peng","year":"2023","journal-title":"IEEE Trans Nanobiosci"},{"key":"2025011303095190600_ref7","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac234","article-title":"Cell\u2013cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies","volume":"23","author":"Peng","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025011303095190600_ref8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-30755-0","article-title":"Comparison of methods and resources for cell-cell communication inference from single-cell RNA-seq data","volume":"13","author":"Dimitrov","year":"2022","journal-title":"Nat Commun"},{"key":"2025011303095190600_ref9","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1093\/bioinformatics\/btab036","article-title":"Cellinker: a platform of ligand\u2013receptor interactions for intercellular communication analysis","volume":"37","author":"Zhang","year":"2021","journal-title":"Bioinformatics"},{"key":"2025011303095190600_ref10","doi-asserted-by":"publisher","first-page":"100382","DOI":"10.1016\/j.crmeth.2022.100382","article-title":"Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data","volume":"3","author":"Xu","year":"2023","journal-title":"Cell Rep Methods"},{"key":"2025011303095190600_ref11","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1038\/s41467-021-21244-x","article-title":"Dissection of intercellular communication using the transcriptome-based framework icellnet","volume":"12","author":"No\u00ebl","year":"2021","journal-title":"Nat Commun"},{"key":"2025011303095190600_ref12","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/s41576-020-00292-x","article-title":"Deciphering cell\u2013cell interactions and communication from gene expression","volume":"22","author":"Armingol","year":"2021","journal-title":"Nat Rev Genet"},{"key":"2025011303095190600_ref13","doi-asserted-by":"publisher","first-page":"e55","DOI":"10.1093\/nar\/gkaa183","article-title":"SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics","volume":"48","author":"Cabello-Aguilar","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2025011303095190600_ref14","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1038\/s41596-020-0292-x","article-title":"CellPhoneDB: inferring cell\u2013cell communication from combined expression of multi-subunit ligand\u2013receptor complexes","volume":"15","author":"Efremova","year":"2020","journal-title":"Nat Protoc"},{"key":"2025011303095190600_ref15","first-page":"507871","article-title":"iTALK: an R package to characterize and illustrate intercellular communication","author":"Wang","year":"2019"},{"key":"2025011303095190600_ref16","doi-asserted-by":"publisher","first-page":"4187","DOI":"10.1038\/s41598-022-07959-x","article-title":"Computation and visualization of cell\u2013cell signaling topologies in single-cell systems data using connectome","volume":"12","author":"Raredon","year":"2022","journal-title":"Sci Rep"},{"key":"2025011303095190600_ref17","doi-asserted-by":"publisher","first-page":"5011","DOI":"10.1038\/s41467-020-18873-z","article-title":"Predicting cell-to-cell communication networks using NATMI","volume":"11","author":"Hou","year":"2020","journal-title":"Nat Commun"},{"key":"2025011303095190600_ref18","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-43120-6","article-title":"Robust mapping of spatiotemporal trajectories and cell\u2013cell interactions in healthy and diseased tissues","volume":"14","author":"Pham","year":"2023","journal-title":"Nat Commun"},{"key":"2025011303095190600_ref19","doi-asserted-by":"publisher","first-page":"2084","DOI":"10.1038\/s41467-020-15968-5","article-title":"Inferring spatial and signaling relationships between cells from single cell transcriptomic data","volume":"11","author":"Cang","year":"2020","journal-title":"Nat Commun"},{"key":"2025011303095190600_ref20","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-021-02286-2","article-title":"Giotto: a toolbox for integrative analysis and visualization of spatial expression data","volume":"22","author":"Dries","year":"2021","journal-title":"Genome Biol"},{"key":"2025011303095190600_ref21","first-page":"566182","article-title":"Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data","volume":"13","author":"Tsuyuzaki","year":"2019"},{"key":"2025011303095190600_ref22","doi-asserted-by":"publisher","first-page":"3665","DOI":"10.1038\/s41467-022-31369-2","article-title":"Context-aware deconvolution of cell\u2013cell communication with Tensor-cell2cell","volume":"13","author":"Armingol","year":"2022","journal-title":"Nat Commun"},{"key":"2025011303095190600_ref23","doi-asserted-by":"publisher","first-page":"bbae436","DOI":"10.1093\/bib\/bbae436","article-title":"scHyper: reconstructing cell\u2013cell communication through hypergraph neural networks","volume":"25","author":"Li","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025011303095190600_ref24","doi-asserted-by":"publisher","first-page":"bbae198","DOI":"10.1093\/bib\/bbae198","article-title":"CPPLS-MLP: a method for constructing cell\u2013cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data","volume":"25","author":"Zhang","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025011303095190600_ref25","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The graph neural network model","volume":"20","author":"Scarselli","year":"2009","journal-title":"IEEE Trans Neural Netw"},{"key":"2025011303095190600_ref26","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"ICLR 2017","author":"Kipf","year":"2017"},{"key":"2025011303095190600_ref27","doi-asserted-by":"publisher","first-page":"bbaa269","DOI":"10.1093\/bib\/bbaa269","article-title":"CellTalkDB: a manually curated database of ligand\u2013receptor interactions in humans and mice","volume":"22","author":"Shao","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025011303095190600_ref28","doi-asserted-by":"crossref","first-page":"D605","DOI":"10.1093\/nar\/gkaa1074","article-title":"The string database in 2021: customizable protein\u2013protein networks, and functional characterization of user-uploaded gene\/measurement sets","volume":"49","author":"Shao","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2025011303095190600_ref29","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/j.celrep.2017.12.072","article-title":"Single-cell transcriptional profiling reveals cellular diversity and intercommunication in the mouse heart","volume":"22","author":"Skelly","year":"2018","journal-title":"Cell Rep"},{"key":"2025011303095190600_ref30","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1038\/s41593-019-0491-3","article-title":"Single-cell transcriptomic profiling of the aging mouse brain","volume":"22","author":"Ximerakis","year":"2019","journal-title":"Nat Neurosci"},{"key":"2025011303095190600_ref31","doi-asserted-by":"publisher","first-page":"D506","DOI":"10.1093\/nar\/gky1049","article-title":"UniProt: a worldwide hub of protein knowledge","volume":"47","author":"U. Consortium","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2025011303095190600_ref32","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1093\/bioinformatics\/bty140","article-title":"iFeature: a python package and web server for features extraction and selection from protein and peptide sequences","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"2025011303095190600_ref33","first-page":"315","article-title":"Deep sparse rectifier neural networks","volume":"15","author":"Glorot","year":"2011","journal-title":"Proc Mach Learn Res"},{"key":"2025011303095190600_ref34","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"2025011303095190600_ref35","doi-asserted-by":"crossref","first-page":"D562","DOI":"10.1093\/nar\/gki022","article-title":"NCBI GEO: mining millions of expression profiles\u2013database and tools","volume":"33","author":"Barrett","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2025011303095190600_ref36","doi-asserted-by":"publisher","first-page":"104516","DOI":"10.1016\/j.compbiomed.2021.104516","article-title":"Prediction of protein-protein interaction sites through extreme gradient boosting with kernel principal component analysis","volume":"134","author":"Wang","year":"2021","journal-title":"Comput Biol Med"},{"key":"2025011303095190600_ref37","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.chemolab.2019.06.003","article-title":"Predicting protein-protein interactions through lightgbm with multi-information fusion","volume":"191","author":"Chen","year":"2019","journal-title":"Chemom Intel Lab Syst"},{"key":"2025011303095190600_ref38","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1109\/TCBB.2021.3061300","article-title":"Deep neural network and extreme gradient boosting based hybrid classifier for improved prediction of protein-protein interaction","volume":"19","author":"Mahapatra","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025011303095190600_ref39","doi-asserted-by":"publisher","first-page":"1392","DOI":"10.1126\/science.273.5280.1392","article-title":"Crystal structure of the aequorea Victoria green fluorescent protein","volume":"273","author":"Ormo","year":"1996","journal-title":"Science"},{"key":"2025011303095190600_ref40","doi-asserted-by":"publisher","first-page":"D282","DOI":"10.1093\/nar\/gkw962","article-title":"Protein Data Bank Japan (PDBJ): updated user interfaces, resource description framework, analysis tools for large structures","volume":"45","author":"Kinjo","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2025011303095190600_ref41","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/978-1-0716-3441-7_5","article-title":"GRAMM web server for protein docking","volume":"2714","author":"Singh","year":"2023","journal-title":"Methods Mol Biol"},{"key":"2025011303095190600_ref42","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.jmb.2007.05.022","article-title":"Protein interfaces, surfaces and assemblies service Pisa at european bioinformatics institute","volume":"372","author":"Krissinel","year":"2007","journal-title":"J Mol Biol"},{"key":"2025011303095190600_ref43","doi-asserted-by":"crossref","first-page":"eabf1356","DOI":"10.1126\/sciadv.abf1356","article-title":"CytoTalk: de novo construction of signal transduction networks using single-cell transcriptomic data","volume":"7","author":"Hu","year":"2021","journal-title":"Sci Adv"},{"key":"2025011303095190600_ref44","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1038\/s41467-021-21246-9","article-title":"Inference and analysis of cell-cell communication using cellchat","volume":"12","author":"Jin","year":"2021","journal-title":"Nat Commun"},{"key":"2025011303095190600_ref45","doi-asserted-by":"publisher","first-page":"3108","DOI":"10.3390\/cancers12113108","article-title":"Cancer-associated fibroblasts: Understanding their heterogeneity","volume":"12","author":"Louault","year":"2020","journal-title":"Cancer"},{"key":"2025011303095190600_ref46","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1038\/nri3084","article-title":"Towards a systems understanding of MHC Class I and MHC Class II antigen presentation","volume":"11","author":"Neefjes","year":"2011","journal-title":"Nat Rev Immunol"},{"key":"2025011303095190600_ref47","doi-asserted-by":"publisher","first-page":"D1053","DOI":"10.1093\/nar\/gkad933","article-title":"STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization","volume":"52","author":"Xu","year":"2023","journal-title":"Nucleic Acids Res"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbae716\/61416164\/bbae716.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbae716\/61416164\/bbae716.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T03:10:32Z","timestamp":1736737832000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae716\/7952007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,11,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae716","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,1]]},"published":{"date-parts":[[2024,11,22]]},"article-number":"bbae716"}}