Machine learning techniques for single nucleotide polymorphism - disease classification models in schizophrenia

Vanessa Aguiar-Pulido, José A. Seoane, Juan R. Rabuñal, Julián Dorado, Alejandro Pazos, Cristian R. Munteanu

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Single nucleotide polymorphisms (SNPs) can be used as inputs in disease computational studies such as pattern searching and classification models. Schizophrenia is an example of a complex disease with an important social impact. The multiple causes of this disease create the need of new genetic or proteomic patterns that can diagnose patients using biological information. This work presents a computational study of disease machine learning classification models using only single nucleotide polymorphisms at the HTR2A and DRD3 genes from Galician (Northwest Spain) schizophrenic patients. These classification models establish for the first time, to the best knowledge of the authors, a relationship between the sequence of the nucleic acid molecule and schizophrenia (Quantitative Genotype - Disease Relationships) that can automatically recognize schizophrenia DNA sequences and correctly classify between 78.3-93.8% of schizophrenia subjects when using datasets which include simulated negative subjects and a linear artificial neural network.

Original languageEnglish (US)
Pages (from-to)4875-4889
Number of pages15
JournalMolecules
Volume15
Issue number7
DOIs
StatePublished - Jul 2010
Externally publishedYes

Keywords

  • Artificial neural networks
  • DNA molecule
  • Evolutionary computation
  • SNP
  • Schizophrenia

ASJC Scopus subject areas

  • Analytical Chemistry
  • Chemistry (miscellaneous)
  • Molecular Medicine
  • Pharmaceutical Science
  • Drug Discovery
  • Physical and Theoretical Chemistry
  • Organic Chemistry

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