.: Marcos Gestal :.     -----


Datos de la Publicación:

Vanessa Aguiar, Marcos Gestal, Carlos Fernandez-Lozano, Daniel Rivero, Cristian Robert Munteanu
Título: Applied Computational Techniques on Schizophrenia using genetic mutations
Revista: Current Topics in Medicinal Chemistry
ISSN: 1568-0266
Volumen: 13 (5)
Páginas: 675-684
Editorial: Bentham Science Publishers
Fecha Publicación: Abril 2013
Factor de Impacto: 3.453
SCIMago Journal Rank: 1.170
PubMed ID: 23548028
doi: 10.2174/1568026611313050010
Categorías WoS: Chemistry, Medicinal - Cuartil: Q1 - Tercil: T1 - Posición 11 de 58


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