{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T04:49:02Z","timestamp":1783831742074,"version":"3.55.0"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:00:00Z","timestamp":1711411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62332020"],"award-info":[{"award-number":["62332020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62350004"],"award-info":[{"award-number":["62350004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2041"],"award-info":[{"award-number":["U22A2041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Major Project of Changsha","award":["kh2202004"],"award-info":[{"award-number":["kh2202004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Studying the molecular heterogeneity of cancer is essential for achieving personalized therapy. At the same time, understanding the biological processes that drive cancer development can lead to the identification of valuable therapeutic targets. Therefore, achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway\u2013pathway interaction network. We train GraphPath to classify the cancer status of patients with prostate cancer based on their multi-omics profiling. Experiment results show that our method outperforms P-NET and other baseline methods. Besides, two external cohorts are used to validate that the model can be generalized to unseen samples with adequate predictive performance. We reduce the dimensionality of latent pathway embeddings and visualize corresponding classes to further demonstrate the optimal performance of the model. Additionally, since GraphPath\u2019s predictions are interpretable, we identify target cancer-associated pathways that significantly contribute to the model\u2019s predictions. Such a robust and interpretable model has the potential to greatly enhance our understanding of cancer\u2019s biological mechanisms and accelerate the development of targeted therapies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/amazingma\/GraphPath.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae165","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T19:55:34Z","timestamp":1711482934000},"source":"Crossref","is-referenced-by-count":17,"title":["GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway\u2013pathway interaction network"],"prefix":"10.1093","volume":"40","author":[{"given":"Teng","family":"Ma","sequence":"first","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 41083 , Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 41083 , Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"2024041105304800000_btae165-B1","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1016\/j.cell.2015.10.025","article-title":"The molecular taxonomy of primary prostate cancer","volume":"163","author":"Abeshouse","year":"2015","journal-title":"Cell"},{"key":"2024041105304800000_btae165-B2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1186\/s12920-020-00829-3","article-title":"Network-based drug 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