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» The Proportional Genetic Algorithm: Gene Expression in a Gen...
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JCB
2007
130views more  JCB 2007»
13 years 8 months ago
Bayesian Inference of MicroRNA Targets from Sequence and Expression Data
MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although mu...
Jim C. Huang, Quaid Morris, Brendan J. Frey
BMCBI
2006
153views more  BMCBI 2006»
13 years 8 months ago
Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments
Background: The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating v...
Maureen A. Sartor, Craig R. Tomlinson, Scott C. We...
GECCO
2008
Springer
175views Optimization» more  GECCO 2008»
13 years 9 months ago
Using differential evolution for symbolic regression and numerical constant creation
One problem that has plagued Genetic Programming (GP) and its derivatives is numerical constant creation. Given a mathematical formula expressed as a tree structure, the leaf node...
Brian M. Cerny, Peter C. Nelson, Chi Zhou
GECCO
2010
Springer
169views Optimization» more  GECCO 2010»
13 years 11 months ago
Robust symbolic regression with affine arithmetic
We use affine arithmetic to improve both the performance and the robustness of genetic programming for symbolic regression. During evolution, we use affine arithmetic to analyze e...
Cassio Pennachin, Moshe Looks, João A. de V...
GECCO
2005
Springer
142views Optimization» more  GECCO 2005»
14 years 2 months ago
Genetic programming: parametric analysis of structure altering mutation techniques
We hypothesize that the relationship between parameter settings, speci cally parameters controlling mutation, and performance is non-linear in genetic programs. Genetic programmin...
Alan Piszcz, Terence Soule