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

Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry
Vanessa Aguiar-PulidoCristian R Munteanu

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.

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Citations

Feb 22, 2014·Molecular BioSystems·Carlos Fernandez-LozanoCristian R Munteanu
Jan 1, 2014·Health Information Science and Systems·Yingying WangYunpeng Cai
Feb 5, 2014·Journal of Theoretical Biology·Carlos Fernandez-LozanoCristian R Munteanu
Jun 28, 2020·Molecular Psychiatry·Matthew Bracher-SmithValentina Escott-Price
Jul 5, 2021·Biological Chemistry·Nikoletta KatsaouniMarcel H Schulz

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Methods Mentioned

BETA
genotyping

Software Mentioned

SAMPLE
Weka
genomeSIMLA
FREGENE
GENOME
FPG
easyPOP
simuPOP
HAP
MDR

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