A Shiny application for exploring TCGA breast cancer data

CopyClust: A Copy-Number-Based Integrative Cluster Classification Scheme for Breast Cancer Samples

WGCNA meta-analysis of two breast cancer datasets

Map of sites participating in the SCAN-B project.

R/Bioconductor code to reproduce the hypothesis-generating data analyses in Ma17JNCI

Statistical analysis to determine the dominant gene for Invasive Ductal Carcinoma (IDC)

A repository that contains all the code for the interactive Shiny app of the models developed in our work on predicting response to neoadjuvant treatment.

Example: PAM50 molecular subtype classification in the Mainz breast cancer dataset

Prediction and analysis using different machine learning algorithms on the breast cancer dataset.

Differential Gene Expression (DGE) Analysis in Curated Microarray Data of Breast Cancer Subtypes

R/Bioconductor code to reproduce biostatistical analyses in Cunha15CanRes

Scripts for an on-going project on the analysis of the interelationships between apoptotic and autophagic pathways in breast cancer.

Survival Analysis Project on Breast Cancer Patients

This project used machine learning models to detect cancer in biopsies with an accuracy of 98%

BPSI Project 2020 applying machine learning to regulators of patient outcome ER in breast cancer.

This repository contains code for building a random forest model to predict the prognosis of breast cancer patients.