Table 1. overview of studies.

First Author (Year)	Disorder	Machine Learning Methods	Data	Models	Comparators
Aguiar-Pulido et al. (2010; 2013)1	Schizophrenia	AdaBoost, BFTree, DNTB, decision tables, SVM (kernel not reported), naïve Bayes, Bayesian networks, MDR, neural network (RBF, linear, perceptron), evolutionary computation	SNPs	12
Yang et al. (2010)	Schizophrenia	AdaBoost (of SVM (RBF)), SVM (RBF)	SNPs	2
Pirooznia et al. (2012)	Bipolar Disorder	Bayesian networks, random forest, neural network (RBF), SVM (kernel not reported)	SNPs	16	PRS, LR
Li et al. (2014)	Bipolar Disorder, Schizophrenia	LASSO, Ridge, SVM (linear)	SNPs	6
Engchuan et al. (2015)	Autism	Neural network (perceptron), SVM (Linear), random forest, CIF	CNVs	4
Acikel et al. (2016)	Bipolar Disorder	MDR, random forest, k-NN, naïve Bayes	SNPs	5
Guo et al. (2016)	Anorexia nervosa	LASSO, SVM (RBF), GBM	SNPs	3
Laksshman et al. (2017)	Bipolar Disorder	Decision tree, random forest, neural network (CNN)	Exomes	3
Chen et al. (2018)	Schizophrenia	Neural network (perceptron)	PRS	4	PRS, LR
Wang et al. (2018)	Schizophrenia, Bipolar Disorder, Autism	Neural networks (cRBM)	SNPs, gene expression	9	LR
Ghafouri-Fard et al. (2019)	Autism	Neural network (with embedding layer)	SNPs	1
Trakadis et al. (2019)	Schizophrenia	LASSO, random forest, SVM (kernel not reported), GBM (XGBoost)	Exomes	4
Vivian-Griffiths et al. (2019)	Schizophrenia	SVM (linear, RBF)	SNPs	8	PRS

¹Merged in extraction [34, 36]. BFTree: best-first decision tree, CIF: conditional inference forest, cRBM: conditional restricted Boltzmann machine, CNN: convolutional neural network, DNTB: Decision table naïve Bayes, GBM: gradient boosting machine, ?-NN: ^-nearest neighbours, LASSO: least absolute shrinkage and selection operator, LR: logistic regression, MDR: multifactor dimensionality reduction, PRS: polygenic risk score, RBF: radial basis function, SVM: support vector machine