Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/98404
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dc.contributorSchool of Optometryen_US
dc.contributorDepartment of Applied Biology and Chemical Technologyen_US
dc.contributorResearch Centre for SHARP Visionen_US
dc.creatorLang, Ten_US
dc.creatorYang, Men_US
dc.creatorXia, Yen_US
dc.creatorLiu, Jen_US
dc.creatorLi, Yen_US
dc.creatorYang, Len_US
dc.creatorCui, Cen_US
dc.creatorHu, Yen_US
dc.creatorLuo, Yen_US
dc.creatorZou, Den_US
dc.creatorZhou, Len_US
dc.creatorFu, Zen_US
dc.creatorZhou, Qen_US
dc.date.accessioned2023-04-27T01:05:50Z-
dc.date.available2023-04-27T01:05:50Z-
dc.identifier.issn2352-4820en_US
dc.identifier.urihttp://hdl.handle.net/10397/98404-
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.rights© 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.rightsThe following publication Lang, T., et al. (2023). "Development of a molecular feature-based survival prediction model of ovarian cancer using the deep neural network." Genes & Diseases 10(4): 1190-1193 is available at https://doi.org/10.1016/j.gendis.2022.10.011.en_US
dc.titleDevelopment of a molecular feature-based survival prediction model of ovarian cancer using the deep neural networken_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1190en_US
dc.identifier.epage1193en_US
dc.identifier.volume10en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1016/j.gendis.2022.10.011en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationGenes & diseases, July 2023, v. 10, no. 4, p. 1190-1193en_US
dcterms.isPartOfGenes & diseasesen_US
dcterms.issued2023-07-
dc.identifier.scopus2-s2.0-85149710372-
dc.identifier.eissn2352-3042en_US
dc.description.validate202304 bcwwen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera1992-
dc.identifier.SubFormID46248-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
Appears in Collections:Journal/Magazine Article
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