Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic Programming and Evolvable Machines
Discovering Gene Networks with a Neural-Genetic Hybrid
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A platform for the selection of genes in DNA microarraydata using evolutionary algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Ridge regression based hybrid genetic algorithms for multi-locus quantitative trait mapping
International Journal of Bioinformatics Research and Applications
Applying genetic algorithms and support vector machines to the gene selection problem
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - VIII Brazilian Symposium on Neural Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hybrid methods to select informative gene sets in microarray data classification
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Neural networks and temporal gene expression data
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Gene expression rule discovery and multi-objective ROC analysis using a neural-genetic hybrid
International Journal of Data Mining and Bioinformatics
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The major problem for current gene expression analysis techniques is how to identify the handful of genes which contribute to a disease from the thousands of genes measured on gene chips (microarrays). The use of a novel neural-genetic hybrid algorithm for gene expression analysis is described here. The genetic algorithm identifies possible gene combinations for classification and then uses the output from a neural network to determine the fitness of these combinations. Normal mutation and crossover operations are used to find increasingly fit combinations. Experiments on artificial and real-world gene expression databases are reported. The results from the algorithm are also explored for biological plausibility and confirm that the algorithm is a powerful alternative to standard data mining techniques in this domain.