The purpose of this project is to investigate whether we can use classification algorithms to not only predict the presence of different mutations recurrent in tumour cohorts, but also make biologically useful inferences about these mutations' downstream effects.
See experiments/subgrouping_test for the bulk of the code used in
our publication
based on this project, as well as a README explaining how to set up and run
one of the experiment pipelines.
This repository consists of three major parts:
Collecting and processing expression, mutation, and other -omics datasets as well as phenotypes such as drug response.
Custom machine learning tools to predict presence of gene mutations, drug response profiles, etc.
Scripts for running particular analyses.