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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| Format: | Article |
| Language: | English |
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MDPI AG
2010-07-01
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| Series: | Molecules |
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| Online Access: | http://www.mdpi.com/1420-3049/15/7/4875/ |
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doaj-bda2b5a9b78449359201867cd648c18a2020-11-25T00:05:24ZengMDPI AGMolecules1420-30492010-07-011574875488910.3390/molecules15074875Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in SchizophreniaCristian R. MunteanuAlejandro PazosJulián DoradoJuan R. RabuñalVanessa Aguiar-PulidoJosé A. SeoaneSingle 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. http://www.mdpi.com/1420-3049/15/7/4875/DNA moleculeSNPschizophreniaartificial neural networksevolutionary computation |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Cristian R. Munteanu Alejandro Pazos Julián Dorado Juan R. Rabuñal Vanessa Aguiar-Pulido José A. Seoane |
| spellingShingle |
Cristian R. Munteanu Alejandro Pazos Julián Dorado Juan R. Rabuñal Vanessa Aguiar-Pulido José A. Seoane Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in Schizophrenia Molecules DNA molecule SNP schizophrenia artificial neural networks evolutionary computation |
| author_facet |
Cristian R. Munteanu Alejandro Pazos Julián Dorado Juan R. Rabuñal Vanessa Aguiar-Pulido José A. Seoane |
| author_sort |
Cristian R. Munteanu |
| title |
Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in Schizophrenia |
| title_short |
Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in Schizophrenia |
| title_full |
Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in Schizophrenia |
| title_fullStr |
Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in Schizophrenia |
| title_full_unstemmed |
Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in Schizophrenia |
| title_sort |
machine learning techniques for single nucleotide polymorphism—disease classification models in schizophrenia |
| publisher |
MDPI AG |
| series |
Molecules |
| issn |
1420-3049 |
| publishDate |
2010-07-01 |
| description |
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. |
| topic |
DNA molecule SNP schizophrenia artificial neural networks evolutionary computation |
| url |
http://www.mdpi.com/1420-3049/15/7/4875/ |
| work_keys_str_mv |
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1725425236837400576 |