Personalized Medicine: Redefining Cancer Treatment

Deep Learning Project using Keras Deep Learning Library to predict the effect of Genetic Variants to enable personalized Medicine.


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What will you learn

  • Understanding the problem statement

  • Importing the dataset and importing libraries

  • Performing basic EDA and checking for null values

  • Using NLP and Datanalysis simultaneously

  • Merging different data frames together

  • Using Violin and Swarmplot for visualization

  • Using subplots for visualization, frequency plot and histogram

  • Visualizing and picking up most common words

  • Words are plotted to a word cloud using the word_cloud library.

  • Importing necessary NLP libraries for text processing

  • Cleaning text using "Stemming" and "Lemmatization"

  • Tokenization and converting text to sequences

  • Feature scaling and normalization

  • Using "Tfidf Vectorizer" for deriving relationship between different words

  • Importing necessary Keras libraries along with LSTM for making a Deep Learning model

  • Performing train_test_split on the dataset

  • Defining "loss function", "solver" and "accuracy" for model

  • Training the model and making the final predictions

Project Description

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. 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 that contribute to tumor growth (drivers) from the neutral mutations (passengers). 

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 the text-based clinical literature.

For this deep  learning project, MSKCC is making available an expert-annotated knowledge base where world-class researchers and oncologists have manually annotated thousands of mutations.

MSKCC(Memorial Sloan Kettering Cancer Center) is making available an expert-annotated knowledge base where world-class researchers and oncologists have manually annotated thousands of mutations.

In this machine learning project, we are going to develop a Machine Learning algorithm that, using this knowledge base as a baseline, automatically classified genetic variations.

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Curriculum For This Mini Project

02h 44m
02h 26m