ArticleTheScientificWorldJournal2013
A robust hybrid approach based on estimation of distribution algorithm and support vector machine for hunting candidate disease genes.
Article in TheScientificWorldJournal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A fast elitism Gaussian estimation of distribution algorithm and application for PID optimization.TheScientificWorldJournal · 2014Article
- Navigating traditional chinese medicine network pharmacology and computational tools.Evidence-based complementary and alternative medicine : eCAM · 2013Article
- Hybrid model based on Genetic Algorithms and SVM applied to variable selection within fruit juice classification.TheScientificWorldJournal · 2013Article
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8 authors.
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Abstract
Microarray data are high dimension with high noise ratio and relatively small sample size, which makes it a challenge to use microarray data to identify candidate disease genes. Here, we have presented a hybrid method that combines estimation of distribution algorithm with support vector machine for selection of key feature genes. We have benchmarked the method using the microarray data of both diffuse B cell lymphoma and colon cancer to demonstrate its performance for identifying key features from the profile data of high-dimension gene expression. The method was compared with a probabilistic model based on genetic algorithm and another hybrid method based on both genetics algorithm and support vector machine. The results showed that the proposed method provides new computational strategy for hunting candidate disease genes from the profile data of disease gene expression. The selected candidate disease genes may help to improve the diagnosis and treatment for diseases.
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