{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:03:28Z","timestamp":1770750208773,"version":"3.50.0"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"vor","delay-in-days":16,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372165"],"award-info":[{"award-number":["62372165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62032007"],"award-info":[{"award-number":["62032007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Recent advances in spatial transcriptomics technologies have provided multi-modality data integrating gene expression, spatial context, and histological images. Accurately identifying spatial domains and spatially variable genes is crucial for understanding tissue structures and biological functions. However, effectively combining multi-modality data to identify spatial domains and determining SVGs closely related to these spatial domains remains a challenge.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we propose spatial transcriptomics multi-modality and multi-granularity collaborative learning (spaMMCL). For detecting spatial domains, spaMMCL mitigates the adverse effects of modality bias by masking portions of gene expression data, integrates gene and image features using a shared graph convolutional network, and employs graph self-supervised learning to deal with noise from feature fusion. Simultaneously, based on the identified spatial domains, spaMMCL integrates various strategies to detect potential SVGs at different granularities, enhancing their reliability and biological significance. Experimental results demonstrate that spaMMCL substantially improves the identification of spatial domains and SVGs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code and data of spaMMCL are available on Github: Https:\/\/github.com\/liangxiao-cs\/spaMMCL.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae607","type":"journal-article","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T16:46:40Z","timestamp":1729183600000},"source":"Crossref","is-referenced-by-count":2,"title":["A multi-modality and multi-granularity collaborative learning framework for identifying spatial domains and spatially variable genes"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3235-4179","authenticated-orcid":false,"given":"Xiao","family":"Liang","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2828-8179","authenticated-orcid":false,"given":"Pei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082,","place":["China"]}]},{"given":"Li","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082,","place":["China"]}]},{"given":"Baiyun","family":"Chen","sequence":"additional","affiliation":[{"name":"Computer Science, Tuskegee University , State of Alabama 36088,","place":["United States"]}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4730-4610","authenticated-orcid":false,"given":"Wanwan","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082,","place":["China"]}]},{"given":"Yongwang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082,","place":["China"]}]},{"given":"Xiangtao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082,","place":["China"]}]},{"given":"Jiawei","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 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