{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T11:25:37Z","timestamp":1787570737654,"version":"build-2736575974"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"Supplement_2","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Netherlands Organization for Scientific Research","award":["24.005.009"],"award-info":[{"award-number":["24.005.009"]}]},{"name":"NWO Aspasia","award":["015.021.065"],"award-info":[{"award-number":["015.021.065"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Spatial computational models such as agent-based models (ABMs) offer powerful in silico tools to study tumor dynamics, yet imaging data are still rarely used to inform these models directly.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present an ABM optimization framework that leverages convolutional encoders to compare spatial patterns between experimental imaging data and ABM-generated outputs within a shared latent space. This quantitative comparison was used to estimate ABM parameters across three datasets, ranging from synthetic data to 3D tumoroid\u2013T cell co-culture microscopy and histopathology images from The Cancer Genome Atlas skin cutaneous melanoma samples. Estimated parameters were evaluated using data-derived features and experimental knowledge, including experimental conditions and gene expressions. Simulations using optimized parameters reproduced key spatial features of the training images, such as tumor boundary complexity and tumor\u2013tumor neighborhood structure. Together, these results demonstrate a flexible framework for ABM parameter optimization using spatial data across modalities, enabling systematic investigation of how spatial architecture influences tumor progression and immune interactions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Source code is available at https:\/\/github.com\/SysBioOncology\/ AutoencoderABM under the GPL-3.0 license, with corresponding data sets at https:\/\/zenodo.org\/records\/19022344.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag444","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T11:41:35Z","timestamp":1782214895000},"source":"Crossref","is-referenced-by-count":0,"title":["Informing agent-based models with spatial data using convolutional 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J","family":"Danen","sequence":"additional","affiliation":[{"name":"Leiden Academic Center for Drug Research, Leiden University , Leiden,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4642-9112","authenticated-orcid":false,"given":"Elsa","family":"Neubert","sequence":"additional","affiliation":[{"name":"Leiden Academic Center for Drug Research, Leiden University , Leiden,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7822-3867","authenticated-orcid":false,"given":"Federica","family":"Eduati","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Eindhoven University of Technology , Eindhoven 5600MB,","place":["The Netherlands"]},{"name":"Institute for Complex Molecular Systems, Eindhoven University of Technology , Eindhoven 5600MB,","place":["The 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