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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
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.