greenelab/pancancer
Building classifiers using cancer transcriptomes across 33 different cancer-types
This project helps cancer researchers and computational biologists build machine learning models to identify specific genetic changes in tumors. Using gene expression data from various cancer types, it takes information about gene mutations and copy number alterations as input and generates a classifier that can detect patterns associated with genes like TP53 or Ras pathway activation. The output is a predictive model that helps understand tumor states across different cancers.
122 stars. No commits in the last 6 months.
Use this if you need to build predictive models to detect specific gene aberrations, like Ras pathway activation or TP53 inactivation, across a range of cancer types using gene expression data.
Not ideal if you are looking for a pre-built, off-the-shelf diagnostic tool, as this project provides the framework and examples for building custom classifiers.
Stars
122
Forks
62
Language
Jupyter Notebook
License
BSD-3-Clause
Category
Last pushed
Apr 30, 2019
Commits (30d)
0
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