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

Molecules

Vanessa Aguiar-Pulido¹  José A. Seoane¹  Juan R. Rabuñal¹  Julián Dorado¹  Alejandro Pazos¹ 

[1] Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, S/N, 15071 A Coruña, Spain

关键词: DNA molecule;    SNP;    schizophrenia;    artificial neural networks;    evolutionary computation;   

DOI  :  10.3390/molecules15074875

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.