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- W1508421933 abstract "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." @default.
- W1508421933 created "2016-06-24" @default.
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- W1508421933 date "2003-01-01" @default.
- W1508421933 modified "2023-10-07" @default.
- W1508421933 title "Genetic Algorithms for Gene Expression Analysis" @default.
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- W1508421933 doi "https://doi.org/10.1007/3-540-36605-9_8" @default.
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