{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T10:14:50Z","timestamp":1786788890810,"version":"3.56.0"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T00:00:00Z","timestamp":1703462400000},"content-version":"vor","delay-in-days":33,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102116"],"award-info":[{"award-number":["62102116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Interdisciplinary Research Foundation of HIT"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option, yet existing methods fall short in extracting deep-level information from pathological images. In this paper, we present THItoGene, a hybrid neural network that utilizes dynamic convolutional and capsule networks to adaptively sense potential molecular signals in histological images for exploring the relationship between high-resolution pathology image phenotypes and regulation of gene expression. A comprehensive benchmark evaluation using datasets from human breast cancer and cutaneous squamous cell carcinoma has demonstrated the superior performance of THItoGene in spatial gene expression prediction. Moreover, THItoGene has demonstrated its capacity to decipher both the spatial context and enrichment signals within specific tissue regions. THItoGene can be freely accessed at https:\/\/github.com\/yrjia1015\/THItoGene.<\/jats:p>","DOI":"10.1093\/bib\/bbad464","type":"journal-article","created":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T02:16:34Z","timestamp":1703556994000},"source":"Crossref","is-referenced-by-count":127,"title":["THItoGene: a deep learning method for predicting spatial transcriptomics from histological images"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0688-4666","authenticated-orcid":false,"given":"Yuran","family":"Jia","sequence":"first","affiliation":[{"name":"Institute for Bioinformatics , School of Computer Science and Technology, , Harbin, 150040 , China"},{"name":"Harbin Institute of Technology , School of Computer Science and Technology, , Harbin, 150040 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junliang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute for Bioinformatics , School of Computer Science and Technology, , Harbin, 150040 , China"},{"name":"Harbin Institute of Technology , School of Computer Science and Technology, , Harbin, 150040 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8200-7681","authenticated-orcid":false,"given":"Li","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Westlake University , Hangzhou, Zhejiang 310024 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1913-081X","authenticated-orcid":false,"given":"Tianyi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Medicine and Health, Harbin Institute of Technology , Harbin, 150040 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6500-6217","authenticated-orcid":false,"given":"Yadong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Medicine 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