Fold Recognition by Predicted Alignment Accuracy
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Sequence-based protein structure prediction using a reduced state-space hidden Markov model
Computers in Biology and Medicine
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
A novel approach to protein structure prediction using PCA or LDA based extreme learning machines
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Protein fold recognition with a two-layer method based on SVM-SA, WP-NN and C4.5 TLM-SNC
International Journal of Data Mining and Bioinformatics
FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds
Computers in Biology and Medicine
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Fold recognition is a challenging field strongly associated with protein function determination, which is crucial for biologists and the pharmaceutical industry. Hidden Markov models (HMMs) have been widely used for this purpose. In this paper we demonstrate how the fold recognition performance of a recently introduced HMM with a reduced state-space topology can be improved. Our method employs an efficient architecture and a low complexity training algorithm based on likelihood maximization. The fold recognition performance of the model is further improved in two steps. In the first step we use a smaller model architecture based on the {E,H,L} alphabet instead of the DSSP secondary structure alphabet. In the second step secondary structure information (predicted or true) is additionally used in scoring the test set sequences. The Protein Data Bank and the annotation of the SCOP database are used for the training and evaluation of the proposed methodology. The results show that the fold recognition accuracy is substantially improved in both steps. Specifically, it is increased by 2.9% in the first step to 22%. In the second step it further increases and reaches up to 30% when predicted secondary structure information is additionally used and it increases even more and reaches up to 34.7% when we use the true secondary structure. The major advantage of the proposed improvements is that the fold recognition performance is substantially increased while the size of the model and the computational complexity of scoring are decreased.