Personalized-Cancer-Diagnosis

1. Description

Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/ Data: Memorial Sloan Kettering Cancer Center (MSKCC) Download training_variants.zip and training_text.zip from Kaggle.

Context: Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/discussion/35336#198462 Problem statement : Classify the given genetic variations/mutations based on evidence from text-based clinical literature.

Real-world/Business objectives and constraints. No low-latency requirement. Interpretability is important. Errors can be very costly. Probability of a data-point belonging to each class is needed.

2. Data

2.1 Data Overview

Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/data We have two data files: one contains the information about the genetic mutations and the other contains the clinical evidence (text) that human experts/pathologists use to classify the genetic mutations. Both these data files are have a common column called ID Data file's information: training_variants (ID , Gene, Variations, Class) training_text (ID, Text)

2.2. Mapping the real-world problem to an ML problem

2.2.1. Type of Machine Learning Problem

There are nine different classes a genetic mutation can be classified into => Multi class classification problem

2.2.2. Performance Metric

Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment#evaluation Metric(s): Multi class log-loss Confusion matrix

Code Execution

• Create the Environment

conda env create -f environment.yml

Activate the environment :

For Windows:

conda activate my_environment

For macOS and Linux:

source activate my_environment

Then run the notebooks available in the directory

• Install the packages directly using

pip install requirements.txt