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» Linear Modeling of Genetic Networks from Experimental Data
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BMCBI
2011
13 years 2 months ago
Learning genetic epistasis using Bayesian network scoring criteria
Background: Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have be...
Xia Jiang, Richard E. Neapolitan, M. Michael Barma...
MSWIM
2005
ACM
14 years 29 days ago
Experimental comparisons between SAODV and AODV routing protocols
There have been various secure routing protocols proposed for mobile ad hoc networks. Most of these protocols are analyzed by two standard techniques: simulation and security anal...
Yuxia Lin, Amir Hamed Mohsenian Rad, Vincent W. S....
GECCO
2007
Springer
192views Optimization» more  GECCO 2007»
13 years 9 months ago
Parsimonious regularization using genetic algorithms applied to the analysis of analytical ultracentrifugation experiments
Frequently in the physical sciences experimental data are analyzed to determine model parameters using techniques known as parameter estimation. Eliminating the effects of noise ...
Emre H. Brookes, Borries Demeler
BMCBI
2007
197views more  BMCBI 2007»
13 years 7 months ago
Boolean networks using the chi-square test for inferring large-scale gene regulatory networks
Background: Boolean network (BN) modeling is a commonly used method for constructing gene regulatory networks from time series microarray data. However, its major drawback is that...
Haseong Kim, Jae K. Lee, Taesung Park
IJAR
2010
152views more  IJAR 2010»
13 years 5 months ago
Structural-EM for learning PDG models from incomplete data
Probabilistic Decision Graphs (PDGs) are a class of graphical models that can naturally encode some context specific independencies that cannot always be efficiently captured by...
Jens D. Nielsen, Rafael Rumí, Antonio Salme...