Personalized Medicine: Redefining Cancer Treatment

In this Personalized Medicine Machine Learning Project you will learn to classify genetic mutations on the basis of medical literature into 9 classes.

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Personalized Medicine Machine Learning Project Template Outcomes

  • Understanding the problem statement
  • Performing basic EDA
  • The various steps in Text Preprocessing
  • Lemmatization
  • Tokenization and converting text to sequences
  • Using "Tfidf Vectorizer" for the deriving relationships between different words
  • Performing training, testing, and validation split on the dataset
  • Multi-Class Classification
  • Understanding Evaluation Metrics
  • Understanding Log Loss and Confusion Matrix
  • Logistic Regression Implementation
  • KNN Implementation
  • Random Forest Classifier Implementation
  • Naive Bayes Classifier Implementation

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Project Description

Overview

Personalized-Medicine-Redefining-Cancer-Treatment is a problem of classifying the given genetic mutations based on the literature available in the medical domain into one of the given nine classes. A lot has been said during the past several years about precision medicine and, more concretely, how genetic testing will 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 contributing to tumor growth (drivers) from the neutral mutations (passengers).

Currently, this interpretation of genetic mutations is being made 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. The machine learning solution to this problem will help to speed up this time-consuming procedure and produce results with significant accuracy.

 

In this project, we create features from medical literature data and develop a machine-learning algorithm that automatically classifies genetic variations using this knowledge base as a baseline.

We will experiment with different models such as Logistic Regression, Random Forest, KNN, and Naive Bayes to find the best-fitting model for the problem statement.



Aim

To classify genetic mutations based on medical literature into the given nine classes.



Data Description 

The dataset is divided into variants and text for training and test datasets, which includes the following features:

  • 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 these mutations)

  • Class (1-9 the class this genetic mutation has been classified on) 

  • Training_text (ID, Text), Text (the clinical evidence used to classify the genetic mutation)




Tech Stack

  • Language: Python

  • Libraries:  pandas, numpy, pretty_confusion_matrix, matplotlib, sklearn, pymongo[srv]



Approach

  • Data Reading

  • Data Analysis

    • Class

    • Gene

    • Variation

  • Text Preprocessing

  • Splitting Data, Evaluation, and Features Extraction

  • Model Building 

    • Logistic Regression

    • Random Forest

    • KNN

    • Naive Bayes

  • Hyperparameter Tuning

    • Logistic Regression

  • Model Evaluation

    • Confusion matrix

    • Log Loss

 

 

Personalized Medicine Machine Learning

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