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» Models for Incomplete and Probabilistic Information
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166
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BMCBI
2005
87views more  BMCBI 2005»
15 years 5 months ago
Efficient decoding algorithms for generalized hidden Markov model gene finders
Background: The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility an...
William H. Majoros, Mihaela Pertea, Arthur L. Delc...
219
Voted
BMCBI
2011
15 years 25 days ago
Using Stochastic Causal Trees to Augment Bayesian Networks for Modeling eQTL Datasets
Background: The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phen...
Kyle C. Chipman, Ambuj K. Singh
159
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BMCBI
2006
119views more  BMCBI 2006»
15 years 5 months ago
Hidden Markov Model Variants and their Application
Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way...
Stephen Winters-Hilt
BMCBI
2010
97views more  BMCBI 2010»
15 years 25 days ago
A semi-parametric Bayesian model for unsupervised differential co-expression analysis
Background: Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined ...
Johannes M. Freudenberg, Siva Sivaganesan, Michael...
LOGCOM
2007
125views more  LOGCOM 2007»
15 years 5 months ago
Epistemic Actions as Resources
We provide algebraic semantics together with a sound and complete sequent calculus for information update due to epistemic actions. This semantics is flexible enough to accommoda...
Alexandru Baltag, Bob Coecke, Mehrnoosh Sadrzadeh