Deep Learning Model for Cancerous Mutations

Development of a Deep Structured Learning Model to Predict the Functional Impacts of Unknown Cancerous Mutations

Deep learning, classified as a sub-field of machine learning, utilizes artificial neural networks that enable computers to learn from observational data by using layers of neuron-like nodes that mimic how human brains analyze information. This project took on the problem of taking on extremely large, yet annotated datasets and enabling the computer to extract raw features and construct a productive tool based on patterns that are buried deep within the data. The data was first queried from the database of the International Cancer Genome Consortium which has identified almost eighty million simple somatic mutations and contains information from change in DNA and type of mutation to clinical evidence and exhibited phenotypic effects—as well as hundreds of potential variations and transcripts, each thousands of nucleotide bases long. After filtering through the highly organized yet partly superficial data, sequestered into a SQLite database, it was reorganized into a series of mutation transcripts connected to a sequence of strings and integers that represented the type of mutation, consequences, functional impact, origin, as well as clinical significance. The model will train on this easier-to-process data and learn features of the data which it will use for predictive analysis. The ultimate goal would to be to provide an unclassified, and extremely large, segment of DNA and have the machine predict features about what type of mutation it is and characteristics about this mutation including, for example, potential loss-of-function that may arise in afflicted patients to better treat these mutations which previously have been silent killers.