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

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 p...

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Bibliographic Details
Published in:Molecules (Basel, Switzerland) Switzerland), 2010-07, Vol.15 (7), p.4875-4889
Main Authors: Aguiar-Pulido, Vanessa, Seoane, José A, Rabuñal, Juan R, Dorado, Julián, Pazos, Alejandro, Munteanu, Cristian R
Format: Article
Language:English
Subjects:
Accuracy
Artificial Intelligence
artificial neural networks
Base Sequence
Classification
Datasets
Disease
DNA molecule
Dopamine
evolutionary computation
Genetic Predisposition to Disease
Humans
Machine learning
Methods
Neural networks
Polymorphism, Single Nucleotide
Proteins
Receptors, Dopamine D3 - genetics
Receptors, Serotonin, 5-HT3 - genetics
Research Design
Schizophrenia
Schizophrenia - diagnosis
Schizophrenia - genetics
SNP
Spain
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Summary: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.
ISSN:1420-3049
1420-3049
DOI:10.3390/molecules15074875