Figure 1. discrimination for all models.

n: number of cases in training set. Studies: a [35], b [40], c [34, 36], d [39], e [25], f [38], g [31], h [30], i [26], j [33], k [37], l [32], m [27]. ¹SVM kernel not reported. ²Modified architecture with intermediate phenotypes in training set only. ³Modified architecture with intermediate phenotypes for training and test sets. ^4,5,6,7Internal and external validation are shown for study l, where validations for the same model are denoted with the same number. ⁸Two-way MDR. ⁹Three-way MDR. ¹⁰Neural network embedding layer. ¹¹Accuracy calculated from confusion matrix. AB: AdaBoost, BN: Bayesian networks, BFTree: best-first tree, CIF: conditional inference forest, cRBM: conditional restricted Boltzmann machine, CI: confidence interval, CNN: convolutional neural network, CNV: copy number variation, DTb: decision tables, DTNB: decision table naïve Bayes, DT: decision tree, EC: evolutionary computation, GE: gene expression, GBM: gradient boosting machine, k-NN: k-nearest neighbours, LASSO: least absolute shrinkage and selection operator, LNN: linear neural network, MDR: multifactor dimensionality reduction, MLP: multi-layer perceptron, NB: naïve Bayes, NN: neural network, PRS: polygenic risk scores, RBF: radial basis function, RF: random forests, SNP: single nucleotide polymorphisms, SVM: support vector machine, XGB: extreme gradient boosting.