{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T01:05:08Z","timestamp":1780016708168,"version":"3.53.1"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"vor","delay-in-days":17,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007431","name":"NRF","doi-asserted-by":"publisher","award":["RS-2026\u201325472402"],"award-info":[{"award-number":["RS-2026\u201325472402"]}],"id":[{"id":"10.13039\/100007431","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"AMED","doi-asserted-by":"publisher","award":["23tk0124003h0001"],"award-info":[{"award-number":["23tk0124003h0001"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"AMED","doi-asserted-by":"publisher","award":["24tk0124003h0002"],"award-info":[{"award-number":["24tk0124003h0002"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"name":"JSPS KAKENHI","award":["JP24H00009"],"award-info":[{"award-number":["JP24H00009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Cell\u2013cell interactions (CCIs) are fundamental to multicellular organisms and play crucial roles in diverse biological processes and disease mechanisms. Understanding CCIs is vital for deciphering disease pathogenesis and developing therapeutic strategies. Although numerous computational methods have been developed to infer CCIs from complex biological data, most existing approaches rely primarily on single-gene expression levels and ligand-receptor databases, often failing to capture the nuanced network-wide changes characteristic of disease states.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>We propose MetaCCI, a novel computational strategy that integrates meta-information into CCI inference by extending the traditional gene expression-based analysis to a gene regulatory network framework. MetaCCI meticulously combines established ligand-receptor pairs with quantitative insights into gene behavior within complex gene networks, enabling the precise extraction of relevant targets for CCI inference. Subsequently, CCI inference was performed using an eigen cell co-expression network, providing a more holistic view of cell\u2013cell communication. Monte Carlo simulations demonstrated that MetaCCI consistently outperforms existing methods in CCI inference. We applied MetaCCI to characterize cell\u2013cell communication in Myelodysplastic Syndromes (MDS). Our results identified distinct interaction patterns in MDS compared with normal cell populations, specifically highlighting the loss of CCIs between \u201cDendritic cells and Hematopoietic precursor cells\u201d and between \u201cDendritic cells and Hematopoietic multipotent progenitor cells\u201d as characteristic features of MDS. Furthermore, FABP5, CD63, and HMGB1 were identified as MDS-specific markers. These findings suggest that diminished CCIs involving dendritic cells, hematopoietic precursor cells, and multipotent progenitor cells are pivotal to MDS pathogenesis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The MetaCCI software is freely available at https:\/\/github.com\/HeewonGitHub\/MetaCCI. An archived version of the software and example datasets used in this study is available at Zenodo: https:\/\/doi.org\/10.5281\/zenodo.20101527.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag313","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T11:30:25Z","timestamp":1778585425000},"source":"Crossref","is-referenced-by-count":0,"title":["MetaCCI: meta cell\u2013cell interaction inference and its application to CCIs characteristics of MDS"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2773-8596","authenticated-orcid":false,"given":"Heewon","family":"Park","sequence":"first","affiliation":[{"name":"School of Mathematics, Statistics and Data Science, Sungshin Women\u2019s University , Seoul, 02844,","place":["Korea"]},{"name":"M&D Data Science Center, Institute of Science Tokyo , Tokyo, 113-8510,","place":["Japan"]},{"name":"Human Genome Center, Institute of Medical Science, 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