{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T04:48:41Z","timestamp":1783831721398,"version":"3.55.0"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Cancer Institute Cancer Center","award":["P30 CA008748"],"award-info":[{"award-number":["P30 CA008748"]}]},{"name":"National Cancer Institute Cancer Center","award":["R21 CA234752"],"award-info":[{"award-number":["R21 CA234752"]}]},{"name":"Air Force Office of Sponsored Research","award":["FA9550-17-1-0435"],"award-info":[{"award-number":["FA9550-17-1-0435"]}]},{"name":"Air Force Office of Sponsored Research","award":["FA9550-20-1-0029"],"award-info":[{"award-number":["FA9550-20-1-0029"]}]},{"DOI":"10.13039\/100001006","name":"Breast Cancer Research Foundation","doi-asserted-by":"publisher","award":["BCRF-17-193"],"award-info":[{"award-number":["BCRF-17-193"]}],"id":[{"id":"10.13039\/100001006","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Science","award":["ICT 2019-0-01601"],"award-info":[{"award-number":["ICT 2019-0-01601"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Convolutional neural networks (CNNs) have achieved great success in the areas of image processing and computer vision, handling grid-structured inputs and efficiently capturing local dependencies through multiple levels of abstraction. However, a lack of interpretability remains a key barrier to the adoption of deep neural networks, particularly in predictive modeling of disease outcomes. Moreover, because biological array data are generally represented in a non-grid structured format, CNNs cannot be applied directly.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address these issues, we propose a novel method, called PathCNN, that constructs an interpretable CNN model on integrated multi-omics data using a newly defined pathway image. PathCNN showed promising predictive performance in differentiating between long-term survival (LTS) and non-LTS when applied to glioblastoma multiforme (GBM). The adoption of a visualization tool coupled with statistical analysis enabled the identification of plausible pathways associated with survival in GBM. In summary, PathCNN demonstrates that CNNs can be effectively applied to multi-omics data in an interpretable manner, resulting in promising predictive power while identifying key biological correlates of disease.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code is freely available at: https:\/\/github.com\/mskspi\/PathCNN.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab285","type":"journal-article","created":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T04:29:22Z","timestamp":1619238562000},"page":"i443-i450","source":"Crossref","is-referenced-by-count":55,"title":["PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma"],"prefix":"10.1093","volume":"37","author":[{"given":"Jung Hun","family":"Oh","sequence":"first","affiliation":[{"name":"Department of Medical Physics, Memorial Sloan Kettering Cancer Center , New York, NY 10065, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wookjin","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Virginia State University , Petersburg, VA 23806, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Euiseong","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Nevada , Las Vegas, NV 89154, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingon","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Nevada , Las Vegas, NV 89154, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Allen","family":"Tannenbaum","sequence":"additional","affiliation":[{"name":"Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University , New York, NY 11794, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph O","family":"Deasy","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, Memorial Sloan Kettering Cancer Center , New York, NY 10065, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,7,12]]},"reference":[{"key":"2023062410180609300_btab285-B1","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1038\/bjc.2017.263","article-title":"Regulation of hypoxia-induced autophagy in glioblastoma involves atg9a","volume":"117","author":"Abdul Rahim","year":"2017","journal-title":"Br. 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