Creating artificial neural networks that generalize
Neural Networks
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Bidirectional segmented-memory recurrent neural network for protein secondary structure prediction
Soft Computing - A Fusion of Foundations, Methodologies and Applications
2005 Special Issue: Learning protein secondary structure from sequential and relational data
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Improving protein secondary structure prediction by using the residue conformational classes
Pattern Recognition Letters
Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
An expert protein loop refinement protocol by molecular dynamics simulations with restraints
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Precise prediction of protein secondary structures from the associated amino acids sequence is of great importance in bioinformatics and yet a challenging task for machine learning algorithms. As a major step toward predicting the ultimate three dimensional structures, the secondary structure assignment specifies the protein function. Considering a multilayer perceptron neural network, pruned for optimum size of hidden layers, as the reference network, advanced kinds of recurrent neural network (RNN) are devised in this article to enhance the secondary structure prediction. To better model the strong correlations between secondary structure elements, types of modular reciprocal recurrent neural networks (MRR-NN) are examined. Additionally, to take into account the long-range interactions between amino acids in formation of the secondary structure, bidirectional RNN are investigated. A multilayer bidirectional recurrent neural network (MBR-NN) is finally applied to capture the predominant long-term dependencies. Eventually, a modular prediction system based on the interactive combination of the MRR-NN and MBR-NN boosts the percentage accuracy (Q"3) up to 76.91% and augments the segment overlap (SOV) up to 68.13% when tested on the PSIPRED dataset. The coupling effects of the secondary structure types as well as the sequential information of amino acids along the protein chain can be well cast by the integration of the MRR-NN and the MBR-NN.