info presentación miembros líneas investigación publicaciones investigación tesis docencia

[Articulos Revista] [Articulos Congreso] [Reports] [Libros] [Capitulos Libro] [Todo] [Resumen]

Categoria WoS
  • Chemistry, Medicinal - Q1 - T1 - 11/58
  • Bioinformatics
  • Evolutionary Computation
Titulo: Applied Computational Techniques on Schizophrenia using genetic mutations
Tipo: revista internacional
Fecha: 4,2013
Revista: Current Topics in Medicinal Chemistry
JCR Journal; Impact Factor: 3.453
SCIMago SJR: 1.170
Volumen: 13 (5)
Paginas: 675-684
ISSN: 1568-0266
Editorial: Bentham Science Publishers
doi: 10.2174/1568026611313050010
Pubmed ID: 23548028


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

    .: SABIA :.  Sistemas Adaptativos y Bioinspirados en Inteligencia Artificial