Applied Computational Techniques on Schizophrenia Using Genetic Mutations
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Applied Computational Techniques on Schizophrenia Using Genetic MutationsAutor(es)
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2013-03-01Cita bibliográfica
Aguiar-Pulido V, Gestal M, Fernández-Lozano C, Rivero D, Munteanu C. Applied computational techniques on schizophrenia using genetic mutations. Curr Top Med Chem. 2013; 13(5):675-684
Resumo
[Abstract] Schizophrenia is a complex disease, with both genetic and environmental influence. Machine learning techniques can be used to associate different genetic variations at different genes with a (schizophrenic or non-schizophrenic) phenotype. Several machine learning techniques were applied to schizophrenia data to obtain the results presented in this study. Considering these data, Quantitative Genotype – Disease Relationships (QDGRs) can be used for disease prediction. One of the best machine learning-based models obtained after this exhaustive comparative study was implemented online; this model is an artificial neural network (ANN). Thus, the tool offers the possibility to introduce Single Nucleotide Polymorphism (SNP) sequences in order to classify a patient with schizophrenia. Besides this comparative study, a method for variable selection, based on ANNs and evolutionary computation (EC), is also presented. This method uses half the number of variables as the original ANN and the variables obtained are among those found in other publications. In the future, QDGR models based on nucleic acid information could be expanded to other diseases.
Palabras chave
Bioinformatics
Data mining
Machine learning
Neural networks
SNP
Schizophrenia
Support vector machines
Data mining
Machine learning
Neural networks
SNP
Schizophrenia
Support vector machines
Versión do editor
Dereitos
The published manuscript is avaliable at EurekaSelect web page.
ISSN
1568-0266