Predicting Duodenal Cancer Risk in Patients with Familial Adenomatous Polyposis Using Machine Learni...
Predicting Duodenal Cancer Risk in Patients with Familial Adenomatous Polyposis Using Machine Learning Model
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AVES
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Language
English
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AVES
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Background/Aims: The aim of this study was to both classify data of familial adenomatous polyposis patients with and without duodenal cancer and to identify important genes that may be related to duodenal cancer by XGboost model. Materials and Methods: The current study was performed using expression profile data from a series of duodenal samples f...
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Predicting Duodenal Cancer Risk in Patients with Familial Adenomatous Polyposis Using Machine Learning Model
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TN_cdi_doaj_primary_oai_doaj_org_article_cf6fc00ca41146f0b6948c27e0981a56
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_cf6fc00ca41146f0b6948c27e0981a56
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ISSN
1300-4948
E-ISSN
2148-5607
DOI
10.5152/tjg.2023.22346
