Schizophrenia is a complex disease, with both genetic and environmental influence. Machine learning techniques
can be used to associate different genetic variations at different genes with a (schizophrenic or non-schizophrenic)
phenotype. Several machine learning techniques were applied to schizophrenia data to obtain the results presented in this
study. Considering these data, Quantitative Genotype – Disease Relationships (QDGRs) can be used for disease prediction.
One of the best machine learning-based models obtained after this exhaustive comparative study was implemented
online; this model is an artificial neural network (ANN). Thus, the tool offers the possibility to introduce Single Nucleotide
Polymorphism (SNP) sequences in order to classify a patient with schizophrenia. Besides this comparative study, a
method for variable selection, based on ANNs and evolutionary computation (EC), is also presented. This method uses
half the number of variables as the original ANN and the variables obtained are among those found in other publications.
In the future, QDGR models based on nucleic acid information could be expanded to other diseases.
Keywords: Bioinformatics, Data mining, Machine learning, Neural networks, Schizophrenia, SNP, Support vector machines.
Rights & PermissionsPrintExport