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Gene selection in autism - Comparative study
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DOI：10.1016/j.neucom.2016.08.123.png)
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The paper investigates application of several methods of feature selection to identification of the most important genes in autism disorder. The study is based on the expression microarray of genes. The applied methods analyze the importance of genes on the basis of different principles of selection. The most important step is to fuse the results of these selections into common set of genes, which are the best associated with autism. These genes may be treated as the biomarkers of this disorder and used in early prediction of autism. The paper proposes and compares three different methods of such fusion: purity of the clusterization space, application of genetic algorithm and random forest in the role of integrator. The numerical experiments are concerned with the identification of the most important biomarkers and their application in autism recognition. They show the applied fusion strategy of many independent selection methods leads to the significant improvement of the autism recognition rate. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Autism
Gene expression microarrays
Feature selection
Clustering
Genetic algorithm
Random forest
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IF:
6.5
论文数:
2.5W
被引数:
6.5W
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