Secondary Protein Structure Prediction Using Neural Networks
This work addresses protein structure prediction for bioinformatics, but it appears incremental as it builds on existing neural network methods without major breakthroughs.
The paper tackles predicting protein secondary structure from amino acid sequences using neural networks, achieving results through experiments on cross-species comparisons, input length variations, and custom error functions, with a proposed recurrent neural network alternative.
In this paper we experiment with using neural network structures to predict a protein's secondary structure (α 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.