Predicting biomarkers from classifier for liver metastasis of colorectal adenocarcinomas using machine learning models

• Published in: Cancer medicine (Malden, MA) Vol. 9; no. 18; pp. 6667 - 6678
• Main Authors: Shuwen, Han, Xi, Yang, Qing, Zhou, Jing, Zhuang, Wei, Wu
• Format: Journal Article
• Language: English
• Published: Bognor Regis John Wiley & Sons, Inc 01.09.2020; John Wiley and Sons Inc; Wiley
• Subjects: Adenocarcinoma; Biomarkers; CatBoost algorithm; Classification; Clinical Cancer Research; colorectal adenocarcinomas; Colorectal cancer; Datasets; feature genes; Learning algorithms; Liver; liver metastasis; machine learning approaches; Menadione; Metastases; Ontology; Original Research; Therapeutic targets

Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However,...