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  • Title: Sequence-based protein structure prediction using a reduced state-space hidden Markov model.
    Author: Lampros C, Costas Papaloukas, Exarchos TP, Yorgos Goletsis, Fotiadis DI.
    Journal: Comput Biol Med; 2007 Sep; 37(9):1211-24. PubMed ID: 17161834.
    Abstract:
    This work describes the use of a hidden Markov model (HMM), with a reduced number of states, which simultaneously learns amino acid sequence and secondary structure for proteins of known three-dimensional structure and it is used for two tasks: protein class prediction and fold recognition. The Protein Data Bank and the annotation of the SCOP database are used for training and evaluation of the proposed HMM for a number of protein classes and folds. Results demonstrate that the reduced state-space HMM performs equivalently, or even better in some cases, on classifying proteins than a HMM trained with the amino acid sequence. The major advantage of the proposed approach is that a small number of states is employed and the training algorithm is of low complexity and thus relatively fast.
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