Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in Schizophrenia

• Published in: Molecules (Basel, Switzerland) Vol. 15; no. 7; pp. 4875 - 4889
• Main Authors: Aguiar-Pulido, Vanessa, Seoane, José A., Rabuñal, Juan R., Dorado, Julián, Pazos, Alejandro, Munteanu, Cristian R.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.07.2010; MDPI
• 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; Spain
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules15074875

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.