{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T05:50:08Z","timestamp":1791352208401,"version":"4.3.1"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Spatial domain identification from spatial transcriptomics (ST) data is essential for characterizing the spatial organization of complex tissues. Existing contrastive learning methods face a persistent trade-off: instance-level approaches are susceptible to false negatives, whereas cluster-level methods may overlook fine-grained local structures. Moreover, direct multi-view fusion may introduce redundant or noisy correlations, thereby degrading representation quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To address these limitations, we propose MGCF-Net, a multi-view granular-ball contrastive fusion network for spatial domain identification. MGCF-Net constructs a spatial graph and a feature graph to represent physical proximity and transcriptomic similarity, respectively. A redundancy-reduction objective preserves spot-level correspondence across the two views while suppressing spurious similarities between different spots. Multi-view granular-ball contrastive learning groups spot-level representations into local structural units and aligns them across views according to their membership overlap.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On eight dorsolateral prefrontal cortex (DLPFC) tissue sections, MGCF-Net attains the highest mean adjusted Rand index (ARI) among the evaluated methods. Its normalized mutual information (NMI) remains competitive with the comparison methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>By contrasting local structural units rather than individual spots, MGCF-Net reduces the influence of false negatives arising from instance-level contrastive learning. This group-level formulation preserves coherent spatial structures without requiring a generative decoder.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2026.1967219","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T04:53:17Z","timestamp":1791348797000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["MGCF-Net: a multi-view granular-ball contrastive fusion network for spatial transcriptomics clustering"],"prefix":"10.3389","volume":"17","author":[{"given":"Li","family":"Zhu","sequence":"first","affiliation":[{"name":"Graduate School of Dalian Medical University","place":["Dalian, Liaoning, China"]},{"name":"State Key Laboratory of Frigid Zone Cardiovascular Disease, Department of Cardiovascular Surgery, General Hospital of Northern Theater Command","place":["Shenyang, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyao","family":"Dong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Frigid Zone Cardiovascular Disease, Department of Cardiovascular Surgery, General Hospital of Northern Theater Command","place":["Shenyang, Liaoning, China"]},{"name":"Postgraduate College, China Medical University","place":["Shenyang, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuji","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Frigid Zone Cardiovascular Disease, Department of Cardiovascular Surgery, General Hospital of Northern Theater Command","place":["Shenyang, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Frigid Zone Cardiovascular Disease, Department of Cardiovascular Surgery, General Hospital of Northern Theater Command","place":["Shenyang, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Frigid Zone Cardiovascular Disease, Department of Cardiovascular Surgery, General Hospital of Northern Theater Command","place":["Shenyang, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huishan","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Frigid Zone Cardiovascular Disease, Department of Cardiovascular Surgery, General Hospital of Northern Theater Command","place":["Shenyang, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"B1","article-title":"VICReg: Variance-invariance-covariance regularization for self-supervised learning","author":"Bardes","year":"2022","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"B2","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proceedings of Machine Learning Research","author":"Chen","year":"2020"},{"key":"B3","doi-asserted-by":"publisher","first-page":"17202","DOI":"10.1109\/TNNLS.2023.3300916","article-title":"A fast granular-ball-based density peaks clustering algorithm for large-scale data","volume":"35","author":"Cheng","year":"2024","journal-title":"IEEE Trans. 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