AI-based pathology predicts origins for cancers of unknown primary - Nature

A unified downloader+preprocessor for cancer genomics datasets

Some accessible radiomics datas were provided in this link.

Data analysis scripts for Rendeiro et. al, 2016 (doi:10.1038/ncomms11938)

Guided Region Identification based on Density of Genes. Building masks for spatial transcriptomics data

Classifying Breast Cancer Molecular Subtypes

DeepResponse: Large Scale Prediction of Cancer Cell Line Drug Response with Deep Learning Based Pharmacogenomic Modelling

Software for the automated analysis of in cellulo high-throughput drug screening

An example of predicting breast cancer using existing data to learn with decision trees (scikit-learn/python)

Using Deep Learning to enhance cancer treatment. Project presented at the 2018 Startupfest hackathon.

More detailed growth models using inference.

NeoGuider, neoepitope detection using advanced feature engineering

Building a machine learning project named Breast Cancer Classification using Python 3.6, IPython Notebook and Python Virtual Environment

Identification of cancer-causing variants

A Markov Decision Process with large number of states and its solvers (value iteration, policy iteration and Q-learning)

This repository implements a machine-learning pipeline that, given a patient’s clinical profile, predicts cancer risk and, if positive, the most likely subtype or metastasis pattern. It includes cleaned training data, reproducible training scripts and a concise analysis report.

Glioblasted is a machine learning model to assist in the detection of glioblastoma multiforme, a high-grade, aggressive form of central nervous system cancer.

Repository for Cancer Disease Response Inference

Deep learning model to distinguish cancer-associated T cell receptors from non-cancer ones

A machine-learning model that uses a convolutional neural network to classify lung tumors in CT scans, which will help detect lung tumors that might have went unnoticed