A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data

PURPOSE: Establish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions’ diagnostic efficiency. METHODS: After deleting the features whose expression level is lower than...

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Detalles Bibliográficos
Autores principales: Chen, Sijie, Zhou, Wenjing, Tu, Jinghui, Li, Jian, Wang, Bo, Mo, Xiaofei, Tian, Geng, Lv, Kebo, Huang, Zhijian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Genetics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886791/
https://www.ncbi.nlm.nih.gov/pubmed/33613644
http://dx.doi.org/10.3389/fgene.2021.632761

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886791/
https://www.ncbi.nlm.nih.gov/pubmed/33613644
http://dx.doi.org/10.3389/fgene.2021.632761

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