Hidden Markov models in computational biology: applications to protein modeling (1994)

by Anders Krogh , Michael Brown , I. Saira Mian , Kimmen Sjölander , David Haussler
Venue:JOURNAL OF MOLECULAR BIOLOGY
Citations:436 - 29 self

Active Bibliography

.2 Kinase experiments – Protein Kinases
105 Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology – Kimmen Sjölander, Kevin Karplus, Michael Brown, Richard Hughey, Anders Krogh, I. Saira Mian, David Haussler - 1996
56 Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families – Michael Brown, Richard Hughey, Anders Krogh, I. Saira Mian, Kimmen Sjölander, David Haussler - 1993
27 Grouping Web Page References into Transactions for Mining World Wide Web Browsing Patterns – R. Cooley, B. Mobasher, J. Srivastava - 1997
9 The Application of Stochastic Context-Free Grammars to Folding, Aligning and Modeling Homologous RNA Sequences – Yasubumi Sakakibara, Michael Brown, Richard Hughey, I. Saira Mian, Kimmen Sjölander, Rebecca C. Underwood, David Haussler - 1993
A Bayesian Approach to Motif-based Protein Modeling – William Noble Grundy, William Noble Grundy - 1998
327 Fitting a mixture model by expectation maximization to discover motifs in biopolymers – Timothy L. Bailey - 1994
23 A flexible motif search technique based on generalized profiles – Philipp Bucher, Kevin Karplus , Nicolas Moeri, Kay Hofmann - 1996
131 Hidden Markov models for sequence analysis: extension and analysis of the basic method – Richard Hughey, Anders Krogh - 1996
9 Discovering Empirically Conserved Amino Acid Substitution Groups in Databases of Protein Families – Thomas D. Wu, Douglas L. Brutlag - 1996
54 Meta-MEME: Motif-based Hidden Markov Models of Protein Families – William N. Grundy, Timothy L. Bailey, Charles P. Elkan, Michael E. Baker - 1997
36 Stochastic Context-Free Grammars for Modeling RNA – Yasubumi Sakakibara, Michael Brown, Rebecca C. Underwood, I. Saira Mian, David Haussler - 1993
2 Bayesian methods in biological sequence analysis – J. S. Liu, T. Logvinenko - 2003
Multiple Sequence Comparison and HMMs – Christian N. S. Pedersen - 2001
5 Parameterization studies of Hidden Markov Models representing highly divergent protein sequences – Marcella A. Mcclure, Rajasekhar Raman - 1995
3 Part 1: Overview of the Probably Approximately Correct (PAC) Learning Framework – David Haussler - 1995
46 Predicting protein structure using hidden Markov models – Kevin Karplus , Kimmen Sjölander , Christian Barrett , Melissa Cline , David Haussler , Richard Hughey , Liisa Holm , Chris Sander - 1997
Stochastic Modeling Tutorial Stochastic Modeling Techniques: Understanding and using hidden Markov models – Leslie Grate, Richard Hughey, Kevin Karplus, Kimmen Sjölander - 1996
166 Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization – Timothy L. Bailey, Charles Elkan - 1995