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IDA
2005
Springer
14 years 1 months ago
Bayesian Networks Learning for Gene Expression Datasets
DNA arrays yield a global view of gene expression and can be used to build genetic networks models, in order to study relations between genes. Literature proposes Bayesian network ...
Giacomo Gamberoni, Evelina Lamma, Fabrizio Riguzzi...
AIPS
2003
13 years 9 months ago
A Multi-Agent System-driven AI Planning Approach to Biological Pathway Discovery
As genomic and proteomic data is collected from highthroughput methods on a daily basis, subcellular components are identified and their in vitro behavior is characterized. Howev...
Salim Khan, William Gillis, Carl Schmidt, Keith De...
BMCBI
2007
164views more  BMCBI 2007»
13 years 7 months ago
Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks
Background: The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a...
Peng Li, Chaoyang Zhang, Edward J. Perkins, Ping G...
ICML
2009
IEEE
14 years 8 months ago
Optimized expected information gain for nonlinear dynamical systems
This paper addresses the problem of active model selection for nonlinear dynamical systems. We propose a novel learning approach that selects the most informative subset of time-d...
Alberto Giovanni Busetto, Cheng Soon Ong, Joachim ...
ISMB
1996
13 years 8 months ago
Parameterization Studies for the SAM and HMMER Methods of Hidden Markov Model Generation
Multiple sequence alignment of distantly related viral proteins remains a challenge to all currently available alignment methods. The hidden Markovmodel approach offers a new,flex...
Marcella A. McClure, Chris Smith, Pete Elton