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Humvar a list of variants with known impact (Benign/Pathogenic) across all diseases

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There are 3 data sources for this assignment:

Humvar – a list of variants with known impact (Benign/Pathogenic) across all diseases

Lab Variants – a list of mutations in HGVS format and the phenotype the patients had

Variants Annotated – The mutations in Lab Variants, annotated with Variant Effects.

 

You must train a Decision Tree using the Humvar dataset, and report the Accuracy, True Positive Rate and False Positive Rate using a Train/Test experiment. Then, using the trained humvar decision tree, predict the outcome of mutations in epilepsy and muscular conditions, again reporting Accuracy, True Positive Rate and False Positive Rate.

Is it easier to predict when a mutation will cause epilepsy compared to predicting if a mutation will cause muscular conditions? How can we tell?

Finally, plot Receiver Operating Curves (ROC) for each disease and explain the interpretation of the ROCs.

 

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