Hidden Markov Models in bioinformatics

Current Bioinformatics

Volumn 2, Issue 1, 2007, Pages 49-61

  De Fonzo, Valeria ^a   Aluffi Pentini, Filippo ^b   Parisi, Valerio ^b  

^a UNIVERSITY OF ROME TOR VERGATA   (Italy)

^b SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Dynamical programming; Hidden Markov Model; HMM; Labeling; Sequence profiling; Structure prediction

Indexed keywords

BIOINFORMATICS; DYNAMIC PROGRAMMING; PATTERN RECOGNITION;

BIOINFORMATICS APPLICATIONS; DYNAMICAL PROGRAMMING; HIDDEN-MARKOV MODELS; LABELINGS; MATHEMATICAL FOUNDATIONS; PROTEIN STRUCTURE PREDICTION; SEQUENCE PROFILING; SOFTWARE-TOOLS; STRUCTURE PREDICTION;

HIDDEN MARKOV MODELS;

AMINO ACID SEQUENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; AUTOMATIC SPEECH RECOGNITION; BACTERIAL GENOME; BIOINFORMATICS; COMPUTER PREDICTION; COMPUTER PROGRAM; DECISION TREE; ESCHERICHIA COLI; FUZZY SYSTEM; GENE MAPPING; GENETIC ALGORITHM; HIDDEN MARKOV MODEL; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MATHEMATICAL MODEL; MAXIMUM LIKELIHOOD METHOD; MOLECULAR PHYLOGENY; NONHUMAN; PRIORITY JOURNAL; PROBABILITY; PROTEIN SECONDARY STRUCTURE; PROTEIN STRUCTURE; REVIEW; RNA STRUCTURE; SEQUENCE ALIGNMENT; SUPPORT VECTOR MACHINE;

EID: 34248188820     PISSN: 15748936     EISSN: None     Source Type: Journal    
DOI: 10.2174/157489307779314348     Document Type: Review

References (70)

1. Rabiner LR. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 1989; 77: 257-86.
2. Eddy SR. What is a hidden Markov model? Nat Biotechnol 2004; 22:1315-6.
3. Durbin R, Eddy S, Krogh A, Mitchison G. Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge, UK 1998.
4. Wu ZB, Hsu LS, Tan CL. A Survey on Statistical Approaches to Natural Language Processing. DISCS Publication No. TRA4/92. Singapore: National University of Singapore, 1992.
5. Charniak E. Statistical techniques for natural language parsing. AI Mag 1997; 18: 33-43.
6. Natarajan P, Elmieh B, Schwartz R, Makhoul J. Videotext OCR using Hidden Markov Models. Proceedings of the Sixth International Conference on Document Analysis and Recognition 2001: 947-51.
7. Pollastri E, Simoncelli G. Classification of Melodies by Composer with Hidden Markov Models. Proceedings of the First International Conference on WEB Delivering of Music WEDELMUSIC'01. IEEE Computer Press, 2001: 88-95.
8. Lander ES, Green P. Construction of multilocus genetic linkage maps in humans. Proc Natl Acad Sci USA 1987; 84:2363-7.
9. Churchill GA. Stochastic models for heterogeneous DNA sequences. Bull Math Biol 1989; 51: 79-94.
10. Eddy SR. Hidden Markov models. Curr Opin Struct Biol 1996; 6: 361-5.
11. Choo KH, Tong JC, Zhang L. Recent applications of Hidden Markov Models in computational biology. Genomics Proteomics Bioinformatics 2004; 2: 84-96.
12. Sonnhammer EL, Eddy SR, Birney E, Bateman A, Durbin R. Pfam: multiple sequence alignments and HMM-profiles of protein domains. Nucleic Acids Res 1998; 26: 320-2.
13. Karplus K, Barrett C, Hughey R. Hidden Markov models for detecting remote protein homologies. Bioinformatics 1998; 14: 846-56.
14. Gough J. The SUPERFAMILY database in structural genomics. Acta Crystallogr D Biol Crystallogr 2002; 58: 1897-900.
15. Truong K, Ikura M. Identification and characterization of subfamily-specific signatures in a large protein superfamily by a hidden Markov model approach. BMC Bioinformatics 2002; 3: 1.
16. Gribskov M, McLachlan AD, Eisenberg D. Profile analysis: Detection of distantly related proteins. Proc Natl Acad Sci USA 1987; 84: 4355-8.
17. Krogh A, Brown M, Mian IS, Sjolander K, Haussler D. Hidden Markov models in computational biology. Applications to protein modeling. J Mol Biol 1994b; 235: 1501-31.
18. Soding J. Protein homology detection by HMM-HMM comparison. Bioinformatics 2005; 21: 951-60.
19. Eddy SR. Profile hidden Markov models. Bioinformatics 1998; 14: 755-63.
20. Slonim D, Kruglyak L, Stein L, Lander E. Building human genome maps with radiation hybrids. J Comput Biol 1997; 4: 487-504.
21. Krogh A, Mian IS, Haussler D. A hidden Markov model that finds genes in E. coli DNA. Nucleic Acids Res 1994; 22: 4768-78.
22. Henderson J, Salzberg S, Fasman KH. Finding genes in DNA with a Hidden Markov Model. J Comput Biol 1997; 4: 127-41.
23. Kulp D, Haussler D, Reese MG, Eeckman FH. A generalized hidden Markov model for the recognition of human genes in DNA. Proc Int Conf Intell Syst Mot Biol 1996; 4: 134-42.
24. Burge CB, Karlin S. Finding the genes in genomic DNA. Curr Opin Struct Biol 1998; 8: 346-54.
25. Meyer IM, Durbin R. Comparative ab initio prediction of gene structures using pair HMMs. Bioinformatics 2002; 18: 1309-18.
26. Pachter L, Alexandersson M, Cawley S. Applications of generalized pair hidden Markov models to alignment and gene finding problems, In: Lengauer T, Sankoff D, Istrail S, Pevzner P, Waterman M Eds, Proceedings of the Fifth International Conference on Computational Biology (RECOMB). Vol. 1, ACM Press, New York, 2001: 241-8.
27. Majoros WH, Pertea M, Salzberg SL. Efficient implementation of a generalized pair hidden Markov model for comparative gene finding. Bioinformatics 2005; 21: 1782-8.
28. Lukashin AV, Borodovsky M. GeneMark.hmm: new solutions for gene finding. Nucleic Acids Res 1998; 26: 1107-15.
29. Pedersen JS, Hein J. Gene finding with a hidden Markov model of genome structure and evolution. Bioinformatics 2003; 19: 219-27.
30. Asai K, Hayamizu S, Handa, K. Prediction of protein secondary structure by the hidden Markov model. Comput Appl Biosci 1993; 9: 141-46.
31. Goldman N, Thorne JL, Jones DT. Using evolutionary trees in protein secondary structure prediction and other comparative sequence analyses. J Mol Biol 1996; 263: 196-208.
32. Juncker AS, Willenbrock H, Von Heijne G, Brunak S, Nielsen H, Krogh A. Prediction of lipoprotein signal peptides in Gramnegative bacteria. Protein Sci 2003; 12: 1652-62.
33. Schneider G, Fechner U. Advances in the prediction of protein targeting signals. Proteomics 2004; 4: 1571-80.
34. Zhang Z, Wood WI. A profile hidden Markov model for signal peptides generated by HMMER. Bioinformatics 2003; 19: 307-8.
35. Sonnhammer EL, von Heijne G, Krogh A. A hidden Markov model for predicting transmembrane helices in protein sequences. Proc Int Conf Intell Syst Mol Biol 1998; 6: 175 -82.
36. Tusnady GE, Simon I. Principles governing amino acid composition of integral membrane proteins: application to topology prediction. J Mol Biol 1998; 283: 489-506.
37. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 2001; 305: 567-80.
38. Kahsay RY, Gao G, Liao L. An improved hidden Markov model for transmembrane protein detection and topology prediction and its applications to complete genomes. Bioinformatics 2005; 21: 1853-8.
39. Martelli PL, Fariselli P, Krogh A, Casadio R. A sequence-profile-based HMM for predicting and discriminating beta barrel membrane proteins. Bioinformatics 2002; 18: S46-53.
40. Liu Q, Zhu YS, Wang BH, Li YX. A HMM-based method to predict the transmembrane regions of beta-barrel membrane proteins. Comput Biol Chem 2003; 27: 69-76.
41. Bagos PG, Liakopoulos TD, Spyropoulos IC, Hamodrakas SJ. A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins. BMC Bioinformatics 2004; 5: 29.
42. Krogh A. Two methods for improving performance of an HMM and their application for gene finding. Proc Int Conf Intell Syst Mol Biol 1997; 5: 179-86.
43. Mamitsuka H. Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 1998; 33: 460-74.
44. Noguchi H, Kato R, Hanai T, Matsubara Y, Honda H, Brusic V, Kobayashi T. Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. J Biosci Bioeng 2002; 94: 264-70.
45. Kato R, Noguchi H, Honda H, Kobayashi T. Hidden Markov model-based approach as the first screening of binding peptides that interact with MHC class II molecules. Enzyme Microb Technol 2003; 33: 472-81.
46. Yu K, Petrovsky N, Schonbach C, Koh JY, Brusic V. Methods for prediction of peptide binding to MHC molecules: a comparative study. Mol Med 2002; 8: 137-48.
47. Udaka K, Mamitsuka H, Nakaseko, Abe N. Prediction of MHC Class I Binding Peptides by a Query Learning Algorithm Based on Hidden Markov Models. J Biol Phys 2002; 28: 183-94.
48. Abe N, Mamitsuka H. Query learning strategies using boosting and bagging. Proceedings of the Fifteenth International Conference on Machine Learning 1998; 1-9.
49. Felsenstein J, Churchill GA. A Hidden Markov Model approach to variation among sites in rate of evolution. Mol Biol Evol 1996; 13: 93-104.
50. Thorne JL, Goldman N, Jones DT. Combining protein evolution and secondary structure. Mol Biol Evol 1996; 13: 666-73.
51. Siepel A, Haussler D. Phylogenetic hidden Markov models. In Nielsen R Ed, Statistical Methods in Molecular Evolution. Springer, New York 2005; 325-351.
52. Husmeier D. Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models. Bioinformatics 2005; 21: II166-72.
53. Mitchison GJ. A probabilistic treatment of phylogeny and sequence alignment. J Mol Evol 1999; 49: 11-22.
54. Qian B, Goldstein RA. Performance of an iterated T-HMM for homology detection. Bioinformatics 2004; 20: 2175-80.
55. Yoon B-J, Vaidyanathan PP. HMM with auxiliary memory: a new tool for modeling RNA secondary structures. Proceedings of the Thirty-Eighth Asilomar Conference on Signals, Systems, and Computers, Monterey, CA, 2004; 2: 1651-5.
56. Eddy SR, Durbin R. RNA sequence analysis using covariance models. Nucleic Acids Res 1994; 22: 2079-88.
57. Eddy SR. A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure. BMC Bioinformatics 2002; 2; 3-18.
58. Knudsen B, Hein J. RNA secondary structure prediction using stochastic context-free grammars and evolutionary history. Bioinformatics 1999; 15: 446-54.
59. Sakakibara Y, Brown M, Hughey R, Mian IS, Sjolander K, Underwood RC, Haussler D. Stochastic context-free grammars for tRNA modeling. Nucleic Acids Res 1994; 25: 5112-20.
60. McCallum A, Freitag D, Pereira F. Maximum entropy Markov models for information extraction and segmentation. Proceedings of the Seventeenth International Conference on Machine Learning 2000: 591-8.
61. McCallum A. Efficiently inducing features of conditional random fields. Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence 2003: 403-10.
62. Lafferty J, McCallum A, Pereira F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of the Eighteen International Conference on Machine Learning 2001: 282-9.
63. Liu H, Wong L. Data Mining Tools for Biological Sequences. J Bioinform Comput Biol 2003;1: 139-68.
64. Tsuda K, Kin T, Asai K. Marginalized kernels for biological sequences. Bioinformatics 2002; 18: 268-75.
65. Boser B, Guyon I, Vapnik V. A training algorithm for optimal margin classifiers. Fifth Annual Workshop on Computational Learning Theory. ACM Press, Pittsburgh, 1992; 144-52.
66. Riis SK, Krogh A. Hidden neural networks: A framework for HMM/NN hybrids. Proceedings of International Conference on Acoustics, Speech and Signal Processing 1997: 3233-6.
67. Krogh A, Riis SK. Hidden neural networks. Neural Comput 1999; 11: 541-63.
68. Baldi P, Chauvin Y. Hybrid modeling, HMM/NN architectures, and protein applications. Neural Comput 1996; 8: 1541-65.
69. Boufounos P, El-Difrawy S, Ehrlich D. Base Calling Using Hidden Markov Models. J Franklin Inst 2004; 341: 23-6.
70. Lin K, Simossis VA, Taylor WR, Meringa J. A simple and fast secondary structure prediction method using hidden neural networks. Bioinformatics 2005; 21: 152-9.