{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T17:37:31Z","timestamp":1794245851298,"version":"4.9.0"},"reference-count":71,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,4,28]],"date-time":"2024-04-28T00:00:00Z","timestamp":1714262400000},"content-version":"vor","delay-in-days":32,"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":["62072124"],"award-info":[{"award-number":["62072124"]}],"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":["62172158"],"award-info":[{"award-number":["62172158"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Guangxi","award":["2023JJG170006"],"award-info":[{"award-number":["2023JJG170006"]}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2023JJ50117"],"award-info":[{"award-number":["2023JJ50117"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science and Technology Innovation Development Foundation of Guangxi University","award":["2022BZRC009"],"award-info":[{"award-number":["2022BZRC009"]}]},{"name":"CAAI-Huawei MindSpore Open Fund","award":["CAAIXSJLJJ-2022-022A"],"award-info":[{"award-number":["CAAIXSJLJJ-2022-022A"]}]},{"name":"Project of Guangxi Key Laboratory of Eye Health","award":["GXYJK-202407"],"award-info":[{"award-number":["GXYJK-202407"]}]},{"name":"Project of Guangxi Health Commission eye and related diseases artificial intelligence screen technology key laboratory","award":["GXYAI-202402"],"award-info":[{"award-number":["GXYAI-202402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes\/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https:\/\/github.com\/lanbiolab\/DeepKEGG.<\/jats:p>","DOI":"10.1093\/bib\/bbae185","type":"journal-article","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T03:43:04Z","timestamp":1712634184000},"source":"Crossref","is-referenced-by-count":141,"title":["DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5839-7504","authenticated-orcid":false,"given":"Wei","family":"Lan","sequence":"first","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology , School of Computer, Electronic and Information, , No. 100 Daxue Road, Xixiangtang District, Nanning 530004 , China"},{"name":"Guangxi University , School of Computer, Electronic and Information, , No. 100 Daxue Road, Xixiangtang District, Nanning 530004 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haibo","family":"Liao","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology , School of Computer, Electronic and Information, , No. 100 Daxue Road, Xixiangtang District, Nanning 530004 , China"},{"name":"Guangxi University , School of Computer, Electronic and Information, , No. 100 Daxue Road, Xixiangtang District, Nanning 530004 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5506-8913","authenticated-orcid":false,"given":"Qingfeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology , School of Computer, Electronic and Information, , No. 100 Daxue Road, Xixiangtang District, Nanning 530004 , China"},{"name":"Guangxi University , School of Computer, Electronic and Information, , No. 100 Daxue Road, Xixiangtang District, Nanning 530004 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingzhi","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Science, Hunan Institute of Technology , No. 18 Henghua Road, Zhuhui District\uff0c Hengyang 421002 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , No. 1068 Xueyuan Avenue, Shenzhen University Town, Nanshan District, Shenzhen 518055 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4122-3767","authenticated-orcid":false,"given":"Yi-Ping Phoebe","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, La Trobe University , Plenty Rd, Bundoora, Melbourne, Victoria 3086 , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"issue":"5","key":"2024042810162967700_ref1","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1109\/TST.2015.7297749","article-title":"Computational approaches for prioritizing candidate disease genes based on PPI networks","volume":"20","author":"Lan","year":"2015","journal-title":"Tsinghua Sci Technol"},{"key":"2024042810162967700_ref2","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1016\/j.csbj.2021.01.009","article-title":"Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis","volume":"19","author":"Menyh\u00e1rt","year":"2021","journal-title":"Comput Struct Biotechnol J"},{"issue":"1","key":"2024042810162967700_ref3","doi-asserted-by":"crossref","first-page":"bbab494","DOI":"10.1093\/bib\/bbab494","article-title":"KGANCDA: predicting 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