Secondary Protein Structure Prediction Using Neural Networks
In this paper we experiment with using neural network structures to predict a protein's secondary structure ({\alpha} helix positions) from only its primary structure (amino acid sequence). We implement a fully connected neural network (FCNN) and preform three experiments using that FCNN. Firstly, we do a cross-species comparison of models trained and tested on mouse and human datasets. Secondly, we test the impact of varying the length of protein sequence we input into the model. Thirdly, we compare custom error functions designed to focus on the center of the input window. At the end of paper we propose a alternative, recurrent neural network model which can be applied to the problem.
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