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» A Sequential Monte Carlo Method for Motif Discovery
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TSP
2008
157views more  TSP 2008»
13 years 5 months ago
Sequential Monte Carlo Methods for Tracking Multiple Targets With Deterministic and Stochastic Constraints
In multitarget scenarios, kinematic constraints from the interaction of targets with their environment or other targets can restrict target motion. Such motion constraint informati...
Ioannis Kyriakides, Darryl Morrell, Antonia Papand...
SAC
2008
ACM
13 years 4 months ago
Particle methods for maximum likelihood estimation in latent variable models
Standard methods for maximum likelihood parameter estimation in latent variable models rely on the Expectation-Maximization algorithm and its Monte Carlo variants. Our approach is ...
Adam M. Johansen, Arnaud Doucet, Manuel Davy
BMCBI
2004
177views more  BMCBI 2004»
13 years 5 months ago
Gapped alignment of protein sequence motifs through Monte Carlo optimization of a hidden Markov model
Background: Certain protein families are highly conserved across distantly related organisms and belong to large and functionally diverse superfamilies. The patterns of conservati...
Andrew F. Neuwald, Jun S. Liu
BMCBI
2008
99views more  BMCBI 2008»
13 years 5 months ago
NestedMICA as an ab initio protein motif discovery tool
Background: Discovering overrepresented patterns in amino acid sequences is an important step in protein functional element identification. We adapted and extended NestedMICA, an ...
Mutlu Dogruel, Thomas A. Down, Tim J. P. Hubbard
JCB
2008
159views more  JCB 2008»
13 years 5 months ago
BayesMD: Flexible Biological Modeling for Motif Discovery
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
Man-Hung Eric Tang, Anders Krogh, Ole Winther