{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T01:20:49Z","timestamp":1772500849784,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T00:00:00Z","timestamp":1751760000000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Project of Shanghai Collaborative Innovation Center of Endoscopy Fudan University, and the Artificial Intelligence Program of the Shanghai Municipal Education Commission","award":["2024RGYB004"],"award-info":[{"award-number":["2024RGYB004"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32300523 and 62132015"],"award-info":[{"award-number":["32300523 and 62132015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Spatial transcriptomics quantifies spatial molecular profiles alongside histology, enabling computational prediction of spatial gene expression distribution directly from whole slide images. Inspired by image-to-text alignment and generation, we introduce Magic, a self-training contrastive learning model designed for histology-to-gene expression prediction. Magic (i) employs contrastive learning to derive shared embeddings for histology and gene expression while utilizing a momentum-based module to generate pseudo-targets to reduce the impact of noise; and (ii) leverages a transformer-based decoder to predict the expression of 300 genes based on histological features. Trained on 75\u00a0760 spots from 56 breast cancer slices and validated on 11\u00a0026 spots from five independent slices, Magic outperforms existing methods in aligning and generating histology\u2013gene expression data, achieving a 10% improvement over the second-best approach. Furthermore, Magic demonstrates robust generalization, effectively predicting gene expression in colorectal cancer samples and The Cancer Genome Atlas (TCGA) datasets through zero-shot learning. Notably, Magic\u2019s predicted gene expression captures interpatient differences, highlighting its strong potential for clinical applications.<\/jats:p>","DOI":"10.1093\/bib\/bbaf317","type":"journal-article","created":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T15:00:38Z","timestamp":1751814038000},"source":"Crossref","is-referenced-by-count":6,"title":["Spatial histology and gene-expression representation and generative learning via online self-distillation contrastive learning"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1634-7971","authenticated-orcid":false,"given":"Qianyi","family":"Yan","sequence":"first","affiliation":[{"name":"School of Life Sciences, Sun Yat-sen University , No. 135, Xingang West Road, Haizhu District, Guangzhou 510275 , Guangdong Province,","place":["China"]},{"name":"College of Information Science and Technology, Donghua University , No. 2999, Renmin North Road, Songjiang District, Shanghai 201620 ,","place":["China"]}]},{"given":"Xuan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University , No. 2699, Qianjin Street, Changchun 130012, Jilin Province ,","place":["China"]}]},{"given":"Jiangnan","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Donghua University , No. 2999, Renmin North Road, Songjiang District, Shanghai 201620 ,","place":["China"]}]},{"given":"Jianming","family":"Rong","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Donghua University , No. 2999, Renmin North Road, Songjiang District, Shanghai 201620 ,","place":["China"]}]},{"given":"Jingsong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Naval Healthcare Information Center, Faculty of Military Health Services, Naval Medical University , No. 800, Xiangyin Road, Pudong New District, Shanghai 200433 ,","place":["China"]}]},{"given":"Pingting","family":"Gao","sequence":"additional","affiliation":[{"name":"Shanghai Collaborative Innovation Center of Endoscopy, Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University , No. 180, Fuxing Road West, Xuhui District, Shanghai 200433 ,","place":["China"]}]},{"given":"Yaochen","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences , No. 320, Yueyang Road, Xuhui District, Shanghai 200031 ,","place":["China"]}]},{"given":"Fufang","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, National Center for Neruological Disorders, Shanghai Key Laboratory of Bran Function and Restoration and Neural Regeneration, Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery , No. 12, Wulumuqi Road West, Xuhui District, Shanghai 200032 ,","place":["China"]}]},{"given":"Chunman","family":"Zuo","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Sun Yat-sen University , No. 135, Xingang West Road, Haizhu District, Guangzhou 510275 , Guangdong Province,","place":["China"]},{"name":"Shanghai Collaborative Innovation Center of Endoscopy, Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University , No. 180, Fuxing Road West, Xuhui District, Shanghai 200433 ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,7,6]]},"reference":[{"key":"2025070611003162200_ref1","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1038\/s41586-021-03634-9","article-title":"Exploring tissue architecture using spatial 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