Hidden Markov model

Fig. 10.22 Hidden Markov model used for protein sequence analysis, are match states (corresponding in this...

Mian, K Sjolander and D Haussler 1994. Hidden Markov Models in Computational Biology. Applications to Protein Modelling. Journal of Molecular Biology 235 1501-1531). 

Having built a hidden Markov model for a particular family of proteins, it can then b< used to search a database. A score is computed for each sequence in the database anc those sequences that score significantly more than other sequences of a similar length ar( identified. This was demonstrated for two key families of proteins, globins and kinases ii the original paper [Krogh et al. 1994]. For the kinases, 296 sequences with a Z score abov<... 

Eddy S R 1996. Hidden Markov Models. Current Opinion in Structural Biology 6 361-365. 

Rabiner L R 1989. A Tutorial on Hidden Markov Models and Selected Applications in Spe Recognition. Fhroceedings cf the IEEE TI-lSl-19rB. 

A Krogh, M Brown, IS Mian, K Sjolander, D Haussler. Hidden Markov models m computational biology Applications to protein modeling. I Mol Biol 235 1501-1531, 1994. 

K Karplus, K Sjolander, C Barrett, M Cline, D Haussler, R Hughey, L Holm, C Sander. Predicting protein structure using hidden Markov models. Proteins Suppl 134-139, 1997. 

TJ Hubbard, J Park. Eold recognition and ab initio structure predictions using hidden Markov models and (I-strand pair potentials. Proteins Struct Eunct Genet 23 398-402, 1995. 

V DiErancesco, J Garnier, PJ Munson. Protein topology recognition from secondary structure sequences Application of the hidden Markov models to the alpha class proteins. J Mol Biol 267 446-463, 1997. 

Building sequence profiles or Hidden Markov Models to perform more sensitive homology searches. A sequence profile contains information about the variability of every sequence position, improving structure prediction methods (secondary structure prediction). Sequence profile searches have become readily available through the introduction of PsiBLAST [4]... 

A Hidden Markov Model (HMM) is a general probabilistic model for sequences of symbols. In a Markov chain, the probability of each symbol depends only on the preceding one. Hidden Markov models are widely used in bioinformatics, most notably to replace sequence profile in the calculation of sequence alignments. 

Heparin Sulfate Proteoglycans Hepatic Lipase Hepatitis Hepatitis C Heptahelical Domain Heptahelical Receptors HERG-channels Heterologous Desensitization Heterologous Expression System Heterotrimeric G-Proteins Hidden Markov Model High-density Lipoprotein (HDL)... 

The parsing of the transporter sequences into the TM domains shown in Fig. 1A represents the consensus result of three different methods. Average hydrophobicity was calculated with ProperTM using different window sizes and the Kyte and Doolittle scale (7). TMHMM, a hidden Markov model-based approach (8), and PHDHTM, a profile-based neural network method (9), were then utilized to refine the predictions. 

Krogh, A., Larsson, B von Heijne, G., and Sonnhammer, E. L. (2001) Predicting transmembrane protein topology with a hidden Markov model application to complete genomes.. /. Mol. Biol. 305, 567-580. 

Baldi, P. (1995). Substitution matrices and hidden Markov models. /. Gomput. Biol. 2, 487-491. 

Baldi, P., Chauvin, Y., Hunkapiller, T., and McClure, M. A. (1994). Hidden Markov models of biological primary sequence information. Proc. Natl. Acad. Sd. U.S.A. 91,1059—1063. 

Brown, M., Hughey, R., Krogh, A., Mian, I. S., Sjolander, K., and Haussler, D. (1993). Using Dirichlet mixture priors to derive hidden Markov models for protein families. Ismb 1, 47-55. 

Eddy, S. R. (1998). Profile hidden Markov models. Bioinformatics 14, 755—763. 

Eddy, S. R., Mitchison, G., and Durbin, R. (1995). Maximum discrimination hidden Markov models of sequence consensus J. Comput. Biol. 2, 9—23. 

Tanaka, H., Ishikawa, M., Asai, K., and Konagaya, A. (1993). Hidden Markov models and iterative aligners study of their equivalence and possibilities. Ismb 1, 395—401. 

Fujiwara, Y., Asogawa, M., and Nakai, K. (1997). Prediction of mitochondrial targeting signals using hidden Markov models. In Genome Informatics 53-60. Miyano, S., and Takagi, T. (eds.) Genome Informatics 1997 Universal Academy Press, Inc. Tokyo, Japan. 

Nielsen, H., and Krogh, A. (1998). Prediction of signal peptides and signal anchors by a hidden Markov model. InteU. Sysl. Mol. Biol. 6, 122-130. 

Stamer, T. Pentland, A. 1995. Visual recognition of american sign language using hidden Markov models. Proc lnt 1 Workshop on Automatic Face- and Gesture-Recognition, Zurich, Switzerland, June 26-28, 1995. 

T., and McClure, M. A., Hidden Markov models of biological primary sequence information, Proc. Natl. 

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