Protein Family Classification with Neural Networks
Understanding protein function from amino acid sequence is a fundamental problem in biology. In this project, we explore how well we can represent biological function through examination of raw sequence alone. Using a large corpus of protein sequences and their annotated protein families, we learn dense vector representations for amino acid sequences using the co occurrence statistics of short fragments. Then, using this representation, we experiment with several neural network architectures to train classifiers for protein family identification. We show good performance for a multi class prediction problem with 589 protein family classes.