Applied Computational Techniques on Schizophrenia Using Genetic Mutations

Abstract

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.

A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis

“…ML models were trained using SNP data and other factors to discover complex patterns and connections between genetic variants and phenotypic outcomes. Using SNP data, the existing models predicted disease risk, treatment response, and other clinical outcomes [ 37 , 38 , 39 , 40 , 41 ]. These models identify high-risk patients or guide personalized therapy.…”

“…SVM model outperformed the other models by achieving an average accuracy of 94.2%. Likewise, Aguiar-Pulido et al [ 38 ] used the ML techniques for Scz detection using genetic mutation. Quantitative genotype–disease relationships were used for disease detection.…”