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» Combining microarrays and genetic analysis
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ISDA
2010
IEEE
13 years 7 months ago
Detecting anomalies in spatiotemporal data using genetic algorithms with fuzzy community membership
A genetic algorithm is combined with two variants of the modularity (Q) network analysis metric to examine a substantial amount fisheries catch data. The data set produces one of t...
Garnett Carl Wilson, Simon Harding, Orland Hoeber,...
BMCBI
2005
126views more  BMCBI 2005»
13 years 9 months ago
Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models
Background: With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases...
Pingzhao Hu, Celia M. T. Greenwood, Joseph Beyene
BMCBI
2006
169views more  BMCBI 2006»
13 years 10 months ago
Finding biological process modifications in cancer tissues by mining gene expression correlations
Background: Through the use of DNA microarrays it is now possible to obtain quantitative measurements of the expression of thousands of genes from a biological sample. This techno...
Giacomo Gamberoni, Sergio Storari, Stefano Volinia
ICDAR
2003
IEEE
14 years 3 months ago
Comparison of Genetic Algorithm and Sequential Search Methods for Classifier Subset Selection
Classifier subset selection (CSS) from a large ensemble is an effective way to design multiple classifier systems (MCSs). Given a validation dataset and a selection criterion, the...
Hongwei Hao, Cheng-Lin Liu, Hiroshi Sako
ICDAR
2003
IEEE
14 years 3 months ago
Feature Selection for Ensembles: A Hierarchical Multi-Objective Genetic Algorithm Approach
Feature selection for ensembles has shown to be an effective strategy for ensemble creation. In this paper we present an ensemble feature selection approach based on a hierarchica...
Luiz E. Soares de Oliveira, Robert Sabourin, Fl&aa...