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

Scientific Reports
Shikha RoyDinesh Gupta

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 enriched model learning, facilitated by different feature selection methods. We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper ins...Continue Reading

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Datasets Mentioned

BETA
GSE61304

Methods Mentioned

BETA
imaging techniques
RNA-seq
Feature
feature extraction

Software Mentioned

Caret
- learn library
intoGen
Bioconductor
R package
WGCNA R
LinSVM
RLASSO
BRCA
TCGA2STAT R package

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