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» Model selection in genetic programming
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CSB
2002
IEEE
169views Bioinformatics» more  CSB 2002»
14 years 1 months ago
Bayesian Network and Nonparametric Heteroscedastic Regression for Nonlinear Modeling of Genetic Network
We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network con...
Seiya Imoto, SunYong Kim, Takao Goto, Sachiyo Abur...
PRICAI
2004
Springer
14 years 2 months ago
Computational Methods for Identification of Human microRNA Precursors
MicroRNA (miRNA), one of non-coding RNAs (ncRNAs), regulates gene expression directly by arresting the messenger RNA (mRNA) translation, which is important for identifying putative...
Jin-Wu Nam, Wha-Jin Lee, Byoung-Tak Zhang
BMCBI
2008
139views more  BMCBI 2008»
13 years 8 months ago
The C1C2: A framework for simultaneous model selection and assessment
Background: There has been recent concern regarding the inability of predictive modeling approaches to generalize to new data. Some of the problems can be attributed to improper m...
Martin Eklund, Ola Spjuth, Jarl E. S. Wikberg
CP
2009
Springer
14 years 9 months ago
A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms
A problem that is inherent to the development and efficient use of solvers is that of tuning parameters. The CP community has a long history of addressing this task automatically. ...
Carlos Ansótegui, Kevin Tierney, Meinolf Se...
BMCBI
2010
178views more  BMCBI 2010»
13 years 8 months ago
Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests
Background: Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example K...
David Edwards, Gabriel C. G. de Abreu, Rodrigo Lab...