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Hidden Markov models in computational biology: applications to protein modeling

by Anders Krogh, Michael Brown, I. Saira Mian, Kimmen Sjölander, David Haussler - JOURNAL OF MOLECULAR BIOLOGY , 1994
"... Hidden.Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated the on globin family, the protein kinase catalytic domain, and the EF-hand calcium binding moti ..."
Abstract - Cited by 655 (39 self) - Add to MetaCart
Hidden.Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated the on globin family, the protein kinase catalytic domain, and the EF-hand calcium binding

Improved methods for building protein models in electron density maps and the location of errors in these models. Acta Crystallogr. sect

by T. A. Jones, J. -y. Zou, S. W. Cowan, M. Kjeldgaard - A , 1991
"... Map interpretation remains a critical step in solving the structure of a macromolecule. Errors introduced at this early stage may persist throughout crystallo-graphic refinement and result in an incorrect struc-ture. The normally quoted crystallographic residual is often a poor description for the q ..."
Abstract - Cited by 1051 (9 self) - Add to MetaCart
for the quality of the model. Strategies and tools are described that help to alleviate this problem. These simplify the model-building process, quantify the goodness of fit of the model on a per-residue basis and locate possible errors in pep-tide and side-chain conformations.

Pfam protein families database

by Robert D. Finn, John Tate, Jaina Mistry, Penny C. Coggill, Stephen John Sammut, Hans-rudolf Hotz, Goran Ceric, Kristoffer Forslund, Sean R. Eddy, Erik L. L. Sonnhammer, Alex Bateman - Nucleic Acids Research, 2008, 36(Database issue): D281–D288
"... Pfam is a comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile hidden Markov models. The current release of Pfam (22.0) contains 9318 protein families. Pfam is now based not only on the UniProtKB sequence database, but also on NCBI GenP ..."
Abstract - Cited by 771 (13 self) - Add to MetaCart
Pfam is a comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile hidden Markov models. The current release of Pfam (22.0) contains 9318 protein families. Pfam is now based not only on the UniProtKB sequence database, but also on NCBI Gen

The Pfam protein families database

by Alex Bateman, Lachlan Coin, Richard Durbin, Robert D. Finn, Volker Hollich, Ajay Khanna, Mhairi Marshall, Simon Moxon, Erik L. L. Sonnhammer, David J. Studholme, Corin Yeats, Sean R. Eddy - Nucleic Acids Res , 2002
"... Pfam is a large collection of protein families and domains. Over the past 2 years the number of families in Pfam has doubled and now stands at 6190 (version 10.0). Methodology improvements for searching the Pfam collection locally as well as via the web are described. Other recent innovations includ ..."
Abstract - Cited by 1070 (39 self) - Add to MetaCart
Pfam is a large collection of protein families and domains. Over the past 2 years the number of families in Pfam has doubled and now stands at 6190 (version 10.0). Methodology improvements for searching the Pfam collection locally as well as via the web are described. Other recent innovations

Biological sequence analysis: probabilistic models of proteins and nucleic acids

by Richard Durbin, Sean Eddy, Anders Krogh, Graeme Mitchison , 1998
"... ..."
Abstract - Cited by 1217 (22 self) - Add to MetaCart
Abstract not found

The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling

by Konstantin Arnold, Lorenza Bordoli, Torsten Schwede, et al. - BIOINFORMATICS , 2005
"... Motivation: Homology models of proteins are of great interest for planning and analyzing biological experiments when no experimental three-dimensional structures are available. Building homology models requires specialized programs and up-to-date sequence and structural databases. Integrating all re ..."
Abstract - Cited by 575 (5 self) - Add to MetaCart
Motivation: Homology models of proteins are of great interest for planning and analyzing biological experiments when no experimental three-dimensional structures are available. Building homology models requires specialized programs and up-to-date sequence and structural databases. Integrating all

Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes

by Anders Krogh, Björn Larsson, Gunnar von Heijne, Erik L. L. Sonnhammer - J. MOL. BIOL , 2001
"... ..."
Abstract - Cited by 899 (17 self) - Add to MetaCart
Abstract not found

Exploiting Generative Models in Discriminative Classifiers

by Tommi Jaakkola, David Haussler - In Advances in Neural Information Processing Systems 11 , 1998
"... Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often resu ..."
Abstract - Cited by 551 (9 self) - Add to MetaCart
vector machines from generative probability models. We provide a theoretical justification for this combination as well as demonstrate a substantial improvement in the classification performance in the context of DNA and protein sequence analysis.

SWISSMODEL: An automated protein homology-modeling server.

by Torsten Schwede , Jü Rgen Kopp , Nicolas Guex , Manuel C Peitsch - Nucleic Acids Res. , 2003
"... ..."
Abstract - Cited by 468 (6 self) - Add to MetaCart
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Fitting a mixture model by expectation maximization to discover motifs in biopolymers.

by Timothy L Bailey , Charles Elkan - Proc Int Conf Intell Syst Mol Biol , 1994
"... Abstract The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expect~tiou ma.,dmization to fit a two-component finite mixture model to the set of sequences. Multiple motifs are found by fitting a mixture model to th ..."
Abstract - Cited by 947 (5 self) - Add to MetaCart
Abstract The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expect~tiou ma.,dmization to fit a two-component finite mixture model to the set of sequences. Multiple motifs are found by fitting a mixture model
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