A lot has been said during the past several years about how precision medicine and, more concretely, how genetic testing is going to disrupt the way diseases like cancer are treated.

But this is only partially happening due to the huge amount of manual work still required. In this project, we will try to take personalized medicine to its full potential.

Once sequenced, a cancer tumor can have thousands of genetic mutations. But the challenge is distinguishing the mutations that contribute to tumor growth (drivers) from the neutral mutations (passengers). 

Currently, this interpretation of genetic mutations is being done manually. This is a very time-consuming task where a clinical pathologist has to manually review and classify every single genetic mutation based on evidence from the text-based clinical literature.

For this deep  learning project, MSKCC is making available an expert-annotated knowledge base where world-class researchers and oncologists have manually annotated thousands of mutations.

MSKCC(Memorial Sloan Kettering Cancer Center) is making available an expert-annotated knowledge base where world-class researchers and oncologists have manually annotated thousands of mutations.

In this machine learning project, we are going to develop a Machine Learning algorithm that, using this knowledge base as a baseline, automatically classified genetic variations.