Personalized Medicine Redefining Cancer Treatment

Statement:-

To classify every single genetic mutation based on evidence from text-based clinical literature using Machine Learning. 

Problem 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. 
Once sequenced, a cancer tumour can have thousands of genetic mutations. But the challenge is distinguishing the mutations 
that contribute to tumour 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 text-based 
clinical literature. 
For this competition MSKCC is making available an expert-annotated knowledge base where world-class researchers and 
oncologists have manually annotated thousands of mutations .We need your help to develop a Machine Learning algorithm that, 
using this knowledge base as a baseline, automatically classifies genetic variations.

Data:

Click here to get the dataset.

1. training_variants - a comma separated file containing the description of the genetic mutations used for training. 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 amino acid change for this mutations), Class (1-9 the class this genetic mutation has been 
classified on) 
2. training_text - a double pipe (||) delimited file that contains the clinical evidence (text) used to classify genetic          
mutations. Fields are 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) 
3. test_variants - a comma separated file containing the description of the genetic mutations used for training. 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 amino acid change for this mutations) 
4.test_text - a double pipe (||) delimited file that contains the clinical evidence (text) used to classify genetic    
mutations. Fields are 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) 
5. submissionSample - a sample submission file in the correct format  

Data Type:-

double (||) pipe delimited and csv files. 
training_variants.csv(Id , Gene, Variations, Class)
training_text(ID, Text) 
test_variants.csv(Id , Gene, Variations) 
test_text (ID, Text) 

Data Size:- 159 MB

Paper:-

1. The Path to Personalized Medicine 
http://www.nejm.org/doi/full/10.1056/nejmp1006304#t=article  

Useful Links :-

1. Kaggle :- https://www.kaggle.com/c/msk-redefining-cancer-treatment 
2. http://onlinelibrary.wiley.com/doi/10.1038/clpt.2012.237/full 
3. https://www.cancer.org/latest-news/personalized-medicine-redefining-cancer-and-its- treatment.html  

Modelling:

Pre-processing of the data for both test and train using different methods(w2vec,Tfidf or bag of words).
models used:
1.Decision tree classifier
2.Logistic Regression 
3.Multinomial Naive Baye's
4.Linear Svc
5.RBF SVM
6.tfidf + Count Vectorizer + Linear SVC
7.K_Nearest_Neighbours