{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:29Z","timestamp":1772138069063,"version":"3.50.1"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"Supplement_2","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NEC Laboratories America"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["#2042159"],"award-info":[{"award-number":["#2042159"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>https:\/\/github.com\/song0309\/asGNN\/<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae383","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T01:33:28Z","timestamp":1718760808000},"page":"ii111-ii119","source":"Crossref","is-referenced-by-count":6,"title":["Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs"],"prefix":"10.1093","volume":"40","author":[{"given":"Tianci","family":"Song","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of Minnesota , Minneapolis, MN 55455, United States"},{"name":"Machine Learning Department, NEC Laboratories America , Princeton, NJ 08540, United States"}]},{"given":"Eric","family":"Cosatto","sequence":"additional","affiliation":[{"name":"Machine Learning Department, NEC Laboratories America , Princeton, NJ 08540, United States"}]},{"given":"Gaoyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Program in Computational Biology and Bioinformatics, Yale University , New Haven, CT 06520, United States"},{"name":"Department of Molecular Biophysics and Biochemistry, Yale University , New Haven, CT 06520, United States"}]},{"given":"Rui","family":"Kuang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Minnesota , Minneapolis, MN 55455, United States"}]},{"given":"Mark","family":"Gerstein","sequence":"additional","affiliation":[{"name":"Program in Computational Biology and Bioinformatics, Yale University , New Haven, CT 06520, United States"},{"name":"Department of Molecular Biophysics and Biochemistry, Yale University , New Haven, CT 06520, United States"},{"name":"Department of Computer Science, Yale University , New Haven, CT 06520, USA"},{"name":"Department of Statistics and Data Science, Yale University , New Haven, CT 06520, USA"},{"name":"Department of Biomedical Informatics and Data Science, Yale University , New Haven, CT 06520, USA"}]},{"given":"Martin Renqiang","family":"Min","sequence":"additional","affiliation":[{"name":"Machine Learning Department, NEC Laboratories America , Princeton, NJ 08540, United States"}]},{"given":"Jonathan","family":"Warrell","sequence":"additional","affiliation":[{"name":"Machine Learning Department, NEC Laboratories America , Princeton, NJ 08540, United States"},{"name":"Program in Computational Biology and Bioinformatics, Yale University , New Haven, CT 06520, United States"},{"name":"Department of Molecular Biophysics and Biochemistry, Yale University , New Haven, CT 06520, United States"}]}],"member":"286","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"2024090413594621200_btae383-B1","doi-asserted-by":"crossref","first-page":"6012","DOI":"10.1038\/s41467-021-26271-2","article-title":"Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","volume":"12","author":"Andersson","year":"2021","journal-title":"Nat Commun"},{"key":"2024090413594621200_btae383-B2","doi-asserted-by":"crossref","first-page":"e1900221","DOI":"10.1002\/bies.201900221","article-title":"Spatially resolved transcriptomes-next generation tools for tissue exploration","volume":"42","author":"Asp","year":"2020","journal-title":"Bioessays"},{"key":"2024090413594621200_btae383-B3","first-page":"51","author":"Cosatto","year":"2013"},{"key":"2024090413594621200_btae383-B4","first-page":"437","author":"Dawood","year":"2021"},{"key":"2024090413594621200_btae383-B5","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.ccell.2023.02.016","article-title":"The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth","volume":"41","author":"de Visser","year":"2023","journal-title":"Cancer Cell"},{"key":"2024090413594621200_btae383-B6","doi-asserted-by":"crossref","first-page":"1706","DOI":"10.1101\/gr.275224.121","article-title":"Advances in spatial transcriptomic data analysis","volume":"31","author":"Dries","year":"2021","journal-title":"Genome Res"},{"key":"2024090413594621200_btae383-B7","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.1136800","article-title":"Clustering by passing 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