{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:40:34Z","timestamp":1759884034264,"version":"build-2065373602"},"reference-count":55,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372143","62072145"],"award-info":[{"award-number":["62372143","62072145"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["LH2019C042"],"award-info":[{"award-number":["LH2019C042"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:p>Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Herein, we present iPRISM (Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling), which is a novel network\u2010based model that integrates multiomics data to predict immunotherapy outcomes. In this approach, iPRISM incorporates gene expression, biological functional network, tumor microenvironment characteristics, immune\u2010related pathways, and clinical data to provide a comprehensive view of factors influencing immunotherapy efficacy. Using stepwise logistic regression, we identified key predictive features and validated iPRISM across multiple cohorts including melanoma, bladder cancer, non\u2010small cell lung cancer, and stomach adenocarcinoma. We also find that iPRISM outperforms the existing methods, achieving high predictive accuracy and demonstrating significant prognostic value for overall and progression\u2010free survival. By identifying key genetic and immunological factors, this model provides a new insight for more personalized treatment strategies and combination therapies to overcome resistance mechanisms. iPRISM can be accessed at CRAN: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/CRAN.R-project.org\/package=iPRISM\">https:\/\/CRAN.R\u2010project.org\/package=iPRISM<\/jats:ext-link>.<\/jats:p>","DOI":"10.1002\/aisy.202400717","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T02:25:09Z","timestamp":1736907909000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["iPRISM: Intelligent Predicting Response to Cancer Immunotherapy through Systematic Modeling"],"prefix":"10.1002","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7503-3128","authenticated-orcid":false,"given":"Yinchun","family":"Su","sequence":"first","affiliation":[{"name":"College of Bioinformatics Science and Technology Harbin Medical University  Harbin 150081 P. 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