Personalized Medicine: Redefining Cancer Treatment

Kaggle - Personalized Medicine: Redefining Cancer Treatment

Problem statement

In this problem we develop algorithms to classify genetic mutations based on clinical evidence (text).

Mapping real world problem to ML problem

• There are nine different classes a genetic mutation can be classified on.
• Multi-Class Classification problem

Business objectives

• The cost of a mis-classification can be very high.
• Provide the probability that each data-point belonging to each of the nine classes for better interpretation.
• Interpretability is important. (Will be useful for Diagnosis)
• No-strict latency requirements.

Performance metric

• Cross entropy
• Confusion matrix

Overview of the Data set

• training_variants provides the information about the genetic mutations.
• training_text provides the clinical evidence (text) that human experts used to classify the genetic mutations. Both are linked via the ID field.

training_variants

A comma separated file containing the description of the genetic mutations used for training.

• 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 : The class (1-9) this genetic mutation has been classified into

training_text

A double pipe (||) delimited file that contains the clinical evidence (text) used to classify genetic mutations.

• 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

Train-Test Split:

• We build train and test by randomly splitting in the ratio of 70:30