The host, Memorial Sloan Kettering Cancer Center (MSKCC), has been maintaining the database OncoKB for the purpose of knowledge sharing of mutation effects among oncologist. 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 text-based clinical literature. And due to my combined interest in biology and data science, I am interested in using my expertise to develop a machine learning algorithm that, using an expert-annotated knowledge base as a baseline, automatically classifies genetic variations.
Note:
- The mentioned kaggle competition is here.
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. 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). The host, Memorial Sloan Kettering Cancer Center (MSKCC) has been maintaining the database OncoKB [1] for the purpose of knowledge sharing of mutation effects among oncologist. 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 text-based clinical literature. Therefore, the task is to develop a Machine Learning algorithm that, using an expert-annotated knowledge base as a baseline, automatically classifies genetic variations.
**Details of input datasets: **
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training_variants - a comma separated file containing the description of the genetic mutations used for training. Fields are ID (the id of the row used to link the mutation to the clinical evidence), Gene (the gene where this genetic mutation is located), Variation (the aminoacid change for this mutations), Class (1-9 the class this genetic mutation has been classified on)
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training_text - a double pipe (||) delimited file that contains the clinical evidence (text) used to classify genetic mutations. Fields are ID (the id of the row used to link the clinical evidence to the genetic mutation), Text (the clinical evidence used to classify the genetic mutation)
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test_variants - a comma separated file containing the description of the genetic mutations used for training. Fields are ID (the id of the row used to link the mutation to the clinical evidence), Gene (the gene where this genetic mutation is located), Variation (the aminoacid change for this mutations)
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test_text - a double pipe (||) delimited file that contains the clinical evidence (text) used to classify genetic mutations. Fields are ID (the id of the row used to link the clinical evidence to the genetic mutation), Text (the clinical evidence used to classify the genetic mutation)