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

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

PurposeEstablish 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.MethodsAfter deleting the features whose expression level is lower than the t...

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Bibliographic Details
Main Authors: Sijie Chen, Wenjing Zhou, Jinghui Tu, Jian Li, Bo Wang, Xiaofei Mo, Geng Tian, Kebo Lv, Zhijian Huang
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-02-01
Series:Frontiers in Genetics
Subjects:
tumor tissue-of-origin
gene expression
XGBoost
feature selection
CUP
Online Access:https://www.frontiersin.org/articles/10.3389/fgene.2021.632761/full

Internet

https://www.frontiersin.org/articles/10.3389/fgene.2021.632761/full

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