Biological Sequence Analysis

Author: Richard Durbin
Publisher: Cambridge University Press
ISBN: 9780521620413
Size: 24.17 MB
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Presents up-to-date computer methods for analysing DNA, RNA and protein sequences.

Problems And Solutions In Biological Sequence Analysis

Author: Mark Borodovsky
Publisher: Cambridge University Press
ISBN: 1139458124
Size: 40.84 MB
Format: PDF, Docs
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Molecular Biology and Evolution 20, 248–254. Dowell, R. D. and Eddy, S. R. (
2004). Evaluation of several lightweight stochastic context-free grammars for
RNA secondary structure prediction. BMC Bioinformatics 5,71–85. Durbin, R.,
Eddy, S., Krogh, A., and Mitchison, G. (1998). Biological Sequence Analysis:
Probabilistic Models of Proteins and Nucleic Acids (Cambridge: Cambridge
University Press). Eddy, S. R. (1998). Profile hidden Markov models.
Bioinformatics 14, 755–763.

Biological Sequence Analysis Using The Seqan C Library

Author: Andreas Gogol-Döring
Publisher: CRC Press
ISBN: 9781420076240
Size: 77.12 MB
Format: PDF, ePub
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Sharcgs, a fast and highly accurate short-read assembly algorithm for de novo
genomic sequencing. Genome Research 17, 1697–1706. Durbin, R., E. R. Sean,
A. Krogh, and G. Mitchison (1999). Biological Sequence Analysis: Probabilistic
Models of Proteins and Nucleic Acids. Cambridge: Cambridge University Press.
Dutheil, J., S. Gaillard, E. Bazin, S. Glemin, V. Ranwez, N. Galtier, and K. Belkhir (
2006). Bio++: a set of C++ libraries for sequence analysis, phylogenetics,
molecular ...

Progress In Artificial Intelligence

Author: Carlos Bento
Publisher: Springer Science & Business Media
ISBN: 3540307370
Size: 26.90 MB
Format: PDF, Kindle
View: 2996
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6. Necia Grant Cooper. The Human Genome Project, Dechiphering the blueprint
of heredity, volume 1. University Science Books, 1994. 7. P. Domingos and M.
Pazzani. Beyond independence: Conditions for the optimality of the simple
bayesian classifier. In International Conference on Machine Learning, pages 105
–112, 1996. 8. E. Eskin, W. N. Grundy, and Y. Singer. Biological sequence
analysis: Probabilistic models of proteins and nucleic acids. Journal of
Computational Biology ...

Computational Intelligence In Medical Informatics

Author: Arpad Kelemen
Publisher: Springer Science & Business Media
ISBN: 3540757678
Size: 30.63 MB
Format: PDF, ePub, Docs
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Nucleic Acids Research (28):235–242 [3] Boeckmann B, Bairoch A, Apweiler R,
Blatter MC, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O'Donovan C, Phan
I, Pilbout S, Schneider M (2003) The SWISS-PROT protein knowledgebase and
its supplement TrEMBL in 2003. Nucleic Acids Res. 31:365–370 [4] Durbin R,
Eddy S, Krogh A, Mitchison G (1998) Biological Sequence Analysis: probabilistic
models of proteins and nucleic acids. Cambridge University Press [5] Koonin EV
and ...

Protein Structure Prediction

Author: Mohammed Zaki
Publisher: Springer Science & Business Media
ISBN: 1588297527
Size: 22.42 MB
Format: PDF, Docs
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Predicting transmembrane protein topology with a hidden Markov model:
application to complete genomes. J Mol Biol, 305(3):567–580. 5. Needleman, S.
and Wunsch, C. (1970). A general method applicable to the search for similarities
in the amino acid sequence of two proteins. J Mol Biol, 48(3):443–453. 6. Durbin,
R., Eddy, S., Krogh, A., and Mitchison, G. (1998). Biological Sequence Analysis:
Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press.
7. Kyte ...

Bioinformatics

Author: Pierre Baldi
Publisher: MIT Press
ISBN: 9780262025065
Size: 50.27 MB
Format: PDF, ePub, Docs
View: 5297
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Pattern Classification and Scene Analysis. John Wiley and Sons, 1973. [154] R.
Durbin, S. R. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis:
Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press,
Cambridge, 1998. [155] S. R. Eddy. Hidden Markov models. Curr. Opin. Struct.
Biol., 6:361-365, 1996. [156] S. R. Eddy and R. Durbin. RNA sequence analysis
using covariance models. Nucl. Acids Res., 22:2079-2088, 1994. [157] S. R.
Eddy, G.

Handbook Of Hidden Markov Models In Bioinformatics

Author: Martin Gollery
Publisher: CRC Press
ISBN: 1420011804
Size: 77.36 MB
Format: PDF, Kindle
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We will cover some of the background about how HMMs came to be used for
biological sequence analysis, but will not spend too much time on the underlying
mathematical theory. For those who are interested in the underlying basis of
Hidden Markov Models and their use in bioinformatics, I recommend the other
excellent books on the subject, particularly “Biological Sequence Analysis:
Probabilistic Models of Proteins and Nucleic Acids” by Durbin, Eddy, Krogh and
Mitchison, and ...

Sequence Evolution Function

Author: Eugene Koonin
Publisher: Springer Science & Business Media
ISBN: 9781402072741
Size: 10.96 MB
Format: PDF, Mobi
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Bioinformatics: a practical guide to the analysis of genes and proteins. John
Wiley & Sons, New York. 3. Mount DW. 2000. Bioinformatics: Sequence and
genome analysis. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY.
Chapter 1. 4. Durbin R, Eddy SR, Krogh A and Mitchison G. 1997. Biological
Sequence Analysis: Probabilistic models of proteins and nucleic acids.
Cambridge University Press, Cambridge, UK. 5. Waterman MS. 1995.
Introduction to Computational ...

Computer Aided Vaccine Design

Author: Joo Chuan Tong
Publisher: Elsevier
ISBN: 1908818417
Size: 33.98 MB
Format: PDF, Docs
View: 1276
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Rabiner, L.R. (1989) A tutorial on hidden Markov models and selected
applications in speech recognition. Proc. IEEE 77: 257–86. Mamitsuka, H. (1989)
Predicting peptides that bind to MHC molecules using supervised learning of
hidden Markov models. Proteins 33: 460–74. Durbin, R., Eddy, S., Krogh, A. and
Mitchison, G. (1998) Biological Sequence Analysis: Probabilistic Models of
Proteins and Nucleic Acids. Cambridge: Cambridge University Press, 51–68.
Akutsu, T. and Sim, K.L. ...