Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning.

ABSTRACT: Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of IDC. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an

SUBMITTER: Roy S 

PROVIDER: S-EPMC7057992 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature