There are 2 repositories under cancer-data topic.
MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets
Targeted and non-targeted anticancer drugs and drug regimens
A Machine Learning Classifier for Lung & Colon Cancer Histopathological Images.
Open-source software pipeline for cancer classification from high-throughput data using machine learning.
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.
Improving Information Extraction from Pathology Reports using Named Entity Recognition
Autoencoders - a deep neural network was used for feature extraction followed by clustering of the "Cancer" dataset using k-means technique
The first GANs-based omics-to-omics translation framework
Bioconductor R-package: Curated Prostate Cancer Data
Predicting chemosensitivity using gene expression
:chart: The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).
Breast Cancer Detection
A Platypus-based variant calling pipeline for cancer data
Exercises solved for the Practical Statistics Module of Udacity's DAND: assignments and practice problems
Feature selection comparison in breath cancer dataset
A comprehensive comparison of decision tree and random forest for cancer classification.
An example of predicting breast cancer using existing data to learn with decision trees (scikit-learn/python)
Helping cancer patients find a second opinion anywhere in the US within seconds.
Examples on processing and working with TCGA mutation and RNA-Seq data
Implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer.
Generative Adversarial Networks for Digital Pathology
Interactive geovisualizations of NY cancer data using Plotly
Open-source command-line pipeline for cancer type classification of high-throughput data using machine learning.
An ML-based project for predicting cancer using Logistic Regression and visualizing performance metrics.
Predict patient survivability after a cancer diagnosis
A partir da Cadeira de Introdução a Ciência de Dados (ICD), com o Professor Yuri Malheiros, na Universidade Federal da Paraíba (UFPB), nós, Adriel, Jessica e Kamily, faremos uma analise dos dados estatísticos de casos de câncer, relacionados a certas idades, a fim de responder perguntas pré-definidas.
The goal of iCTC is to detect whether peripheral blood cells have CTCs (circulating tumor cell) or not.
Computational Biology Coursework centred around exploring cancer data for common genotypic and phenotypic trends
Prediction of Cancer Using Machine Learning Model
Dissertation on Cancer Detection [Prostate Cancer] Research and Study
In this project I will look at a dataset of patient data relating to breast cancer, and develop a machine learning model that will aim to predict Malignant tumors with the highest accuracy.
Scripts to manage biotab files from TCGA.