{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T08:17:53Z","timestamp":1778401073638,"version":"3.51.4"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"vor","delay-in-days":21,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"National Institute of General Medical Sciences of the National Institutes of Health","award":["1R35GM150460"],"award-info":[{"award-number":["1R35GM150460"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Spatial transcriptomics reveals the spatial distribution of genes in complex tissues, providing crucial insights into biological processes, disease mechanisms, and drug development. The prediction of gene expression based on cost-effective histology images is a promising yet challenging field of research. Existing methods for gene prediction from histology images exhibit two major limitations. First, they ignore the intricate relationship between cell morphological information and gene expression. Second, these methods do not fully utilize the different latent stages of features extracted from the images. To address these limitations, we propose a novel hypergraph neural network model, HGGEP, to predict gene expressions from histology images. HGGEP includes a gradient enhancement module to enhance the model\u2019s perception of cell morphological information. A lightweight backbone network extracts multiple latent stage features from the image, followed by attention mechanisms to refine the representation of features at each latent stage and capture their relations with nearby features. To explore higher-order associations among multiple latent stage features, we stack them and feed into the hypergraph to establish associations among features at different scales. Experimental results on multiple datasets from disease samples including cancers and tumor disease, demonstrate the superior performance of our HGGEP model than existing methods.<\/jats:p>","DOI":"10.1093\/bib\/bbae500","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T15:21:11Z","timestamp":1727709671000},"source":"Crossref","is-referenced-by-count":25,"title":["Gene expression prediction from histology images via hypergraph neural networks"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0608-1502","authenticated-orcid":false,"given":"Bo","family":"Li","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology , Faculty of Information Technology, , Beijing, 100124 ,","place":["China"]},{"name":"Beijing Institute of Artificial Intelligence, Beijing University of Technology , Faculty of Information Technology, , Beijing, 100124 ,","place":["China"]}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology , Faculty of Information Technology, , Beijing, 100124 ,","place":["China"]},{"name":"Beijing Institute of Artificial Intelligence, Beijing University of Technology , Faculty of Information Technology, , Beijing, 100124 ,","place":["China"]}]},{"given":"Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University , Jinhua, Zhejiang 321004 ,","place":["China"]}]},{"given":"Chengyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology , Faculty of Information Technology, , Beijing, 100124 ,","place":["China"]},{"name":"Beijing Institute of Artificial Intelligence, Beijing University of Technology , Faculty of Information Technology, , Beijing, 100124 ,","place":["China"]}]},{"given":"Mengran","family":"Li","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Sun Yat-sen University , Guangzhou, Guangdong 510275 ,","place":["China"]}]},{"given":"Guangyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute , Houston, TX 77030 ,","place":["United States"]},{"name":"Department of Cardiothoracic Surgery , Weill Cornell Medicine, , New York, NY 10065 ,","place":["United States"]},{"name":"Cornell University , Weill Cornell Medicine, , New York, NY 10065 ,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4455-5302","authenticated-orcid":false,"given":"Qianqian","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Health Outcomes and Biomedical Informatics , College of Medicine, , FL 32611 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