Data processing and analysis related code associated with the study "A single-cell and spatially resolved atlas of human breast cancers".

Analysis workflow for MRC-IEU PhD mini-project on childhood BMI effect on breast cancer

Data Modelling and Analysis Coursework - UON. the Classification of Breast Cancer

R package providing various functions relevant for gene expression analysis with emphasis on breast cancer.

Code companion to the publication "High resolution mapping of the breast cancer tumor microenvironment using integrated single cell, spatial and in situ analysis of FFPE tissue"

PFA-ScanNet: Pyramidal Feature Aggregation With Synergistic Learning for Breast Cancer Metastasis Analysis (Architecture Only Pytorch Implementation).

We used different machine learning approaches to build models for detecting and visualizing important prognostic indicators of breast cancer survival rate. This repository contains R source codes for 5 steps which are, model evaluation, Random Forest further modelling, variable importance, decision tree and survival analysis. These can be a pipeline for researcher who are interested to conduct studies on survival prediction of any type of cancers using multi model data.

Breast Cancer data analysis

# Breast-cancer-risk-prediction > Necessity, who is the mother of invention. – Plato* ## Welcome to my GitHub repository on Using Predictive Analytics model to diagnose breast cancer. --- ### Objective: The repository is a learning exercise to: * Apply the fundamental concepts of machine learning from an available dataset * Evaluate and interpret my results and justify my interpretation based on observed data set * Create notebooks that serve as computational records and document my thought process. The analysis is divided into four sections, saved in juypter notebooks in this repository 1. Identifying the problem and Data Sources 2. Exploratory Data Analysis 3. Pre-Processing the Data 4. Build model to predict whether breast cell tissue is malignant or Benign ### [Notebook 1](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB1_IdentifyProblem%2BDataClean.ipynb): Identifying the problem and Getting data. **Notebook goal:Identify the types of information contained in our data set** In this notebook I used Python modules to import external data sets for the purpose of getting to know/familiarize myself with the data to get a good grasp of the data and think about how to handle the data in different ways.  ### [Notebook 2](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB2_ExploratoryDataAnalysis.ipynb) Exploratory Data Analysis **Notebook goal:  Explore the variables to assess how they relate to the response variable** In this notebook, I am getting familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) ### [Notebook 3](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB3_DataPreprocesing.ipynb) Pre-Processing the data **Notebook goal:Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** In this notebook I use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. ### [Notebook 4](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB4_PredictiveModelUsingSVM.ipynb) Predictive model using Support Vector Machine (svm) **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). I also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. ### [Notebook 5](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB_5%20OptimizingSVMClassifier.ipynb): Optimizing the Support Vector Classifier **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I aim to tune parameters of the SVM Classification model using scikit-learn.

DiagnoSys is a comprehensive web application that provides advanced detection and analysis for various health conditions. This project leverages state-of-the-art machine learning algorithms to detect and diagnose COVID-19, Alzheimer's disease, breast cancer, and pneumonia using X-ray and MRI datasets.

This project analyzes breast cancer data using Python, employing libraries for data manipulation, visualization, and machine learning. The main objectives are to explore the dataset, visualize insights, and implement classification algorithms to predict breast cancer diagnoses.

Integrative Transcriptome-Histopathology Analysis for Breast Cancer Classification

No description available

A comprehensive machine learning application that predicts breast cancer malignancy using cytology measurements. Features an interactive Streamlit web interface with real-time visualizations including radar charts for cell nuclei analysis. Implements logistic regression with data preprocessing pipelines for accurate benign/malignant classification.

No description available

Code to reproduce tables and figures from Leibig et al. 2022: Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis, The Lancet Digital Health

Multi-omics analysis of breast and ovarian cancer data from National Cancer Institute's TCGA and CPTAC repositories.

This repository consists of the various Jupyter Notebooks that were written to perform analysis on the different Open-Source Datasets available on Health Parameters and different disease, namely: Breast Cancer, Diabetes Analysis, Heart Disease, Kidney Disease and Liver Disease.

Source codes for Integrated single-cell analysis reveals distinct epigenetic-regulated cancer cell states and heterogeneity-guided core signature in tamoxifen-resistant breast cancer

Abstract— This paper presents a machine learning (ML) method for detection and visual analysis of invasive ductal carcinoma (IDC) locations in whole slide images (WSI) of breast cancer. Machine learning is an artificial intelligence approach that learns from the experience consisting of computational methods and statistics to learn information directly from the dataset for modeling the relationships in data. It is a similar approach to how the human brain works by interpreting features such as representative layers.

Deep Learning Analysis of Gene Expression Data for Breast Cancer Classification

A backup for my undergraduate thesis.

A machine learning project that uses logistic regression to classify tumors as benign or malignant based on the Breast Cancer Wisconsin dataset. This project demonstrates data preprocessing, exploratory data analysis, model training, and evaluation using metrics like accuracy, confusion matrix, and ROC-AUC.

DrGA is a novel R package that has been developed based on the idea of our recent driver gene analysis scheme. It wholly automates the analysis process and attached improvements to maximize user experience with the highest convenience. In particular, it facilitates users with limited IT backgrounds and rapidly creates consistent and reproducible results. We describe the usage of the DrGA on driver genes of human breast cancer using a multi-omics dataset. Besides, we also provide users with another potential application of DrGA on analyzing genomic biomarkers of a complex disease from other species.

#machine learning #python #data preprocessing #breast cancer

Breast Cancer Dataset Analysis using RStudio

Predicts the recurrence of Breast cancer using ML and DL algorithms.

Survival Analysis of Breast Cancer Data (BRCA) from the TCGA Dataset

Intro to spatial omics analysis using the VoltRon and Seurat R packages on Visium and Xenium breast cancer tumor microenvironment datasets during the compgen2025 2nd module, taught by Artur Manukyan.

Comparative analysis of CNN architectures on Breast Ultrasound Images Dataset. Explored 9 CNN models for classifying images into normal, benign, malignant categories. MobileNet and Inception-ResNet-V2 emerged as top performers with 83% and 80% accuracy, respectively. Insights aid in early breast cancer detection.