In this talk, I will discuss the use of natural language processing methods to read scientific literature and learn the implicit features that cause certain genetic mutations to be pathogenic. Precisely, our method ingests the bio-medical literature and produces its fixed representation via exploiting state of the art NLP methods like word2vec and tf-idf. 

These representations are then fed to machine learning predictors to identify the pathogenic versus neutral variations. I will also discuss the use of deep learning methods to classify breast cancer into four classes. I will discuss the use of scaled networks to diagnose the various stages of breast cancer. Both of our methodologies significantly outperform previous state-of-the-art studies on publicly available datasets.