Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Fold Recognition by Predicted Alignment Accuracy
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Protein Fold Recognition using a Structural Hidden Markov Model
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Sequence-based protein structure prediction using a reduced state-space hidden Markov model
Computers in Biology and Medicine
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In protein fold recognition, the main disadvantage of hidden Markov models (HMMs) is the employment of large-scale model architectures which require large data sets and high computational resources for training. Also, HMMs must consider sequential information about secondary structures of proteins, to improve prediction performance and reduce model parameters. Therefore, we propose a novel method for protein fold recognition based on a hidden Markov model, called a 9-state HMM. The method can (i) reduce the number of states using secondary structure information about proteins for each fold and (ii) recognize protein folds more accurately than other HMMs.