换一批Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer.
第一作者：
Anamika,Thalor
第一单位：
Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India.
作者：
关键词
AUC, Area under the ROC curveBrCa, Breast cancerCOSMIC, The catalogue of somatic mutations in cancerCX-25, Complete XgBoost top 25DE, Differential ExpressionDMFS, Distasnt metastasis free survivalDX-20, Driver XgBoost top 20Differential gene expressionDistant-metastasis free survivalEMT, Epithelial to mesenchymal transitionER, Oestrogen ReceptorFDR, False discovery rateGEO, Gene expression omnibousHER2, Human epidermal growth factor receptor 2KM, Kaplan MeierML, Machine learningNSCLC, Non small cell lung carcinomaOS, Overall survivalPCA, Principal component analysisPOU2AF1PR, Progesterone receptorPrognostic gene signaturesRF, Random forestRFE, Recursive feature eliminationROC, Receiver operating characteristics curveS100BSVM, Support vector machineTNBCTNBC, Triple negative breast cancerkNN, k Nearest neighbors
DOI
10.1016/j.csbj.2022.03.019
PMID
35465161
发布时间
2022-07-16
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