Applied computational techniques on schizophrenia using genetic mutations

Vanessa Aguiar-Pulido, Marcos Gestal, Carlos Fernandez-Lozano, Daniel Rivero, Cristian R. Munteanu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 Nucleo-tide 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.

Original languageEnglish (US)
Pages (from-to)675-684
Number of pages10
JournalCurrent Topics in Medicinal Chemistry
Volume13
Issue number5
DOIs
StatePublished - 2013
Externally publishedYes

Keywords

  • Bioinformatics
  • Data mining
  • Machine learning
  • Neural networks
  • Schizophrenia
  • SNP
  • Support vector machines

ASJC Scopus subject areas

  • Drug Discovery

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