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Fig. 2 | Genome Medicine

Fig. 2

From: DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype–phenotype prediction

Fig. 2

Schizophrenia classification and functional genomic prioritization using genotype and bulk-tissue gene expression data. The population data was from the PsychENCODE project (Methods and materials). A Balanced accuracies from 5-fold cross-validation and B receiver operating characteristic (ROC) curves of DeepGAMI dual-modality model (dark blue), DeepGAMI single modality model (orange), Lasso (brown), LR (light blue), Random Forest (yellow), SVM (purple), Multilayer perceptron (MLP, pink), Varmole (red), and MOGONET (green) for classifying schizophrenia vs. control individuals on the held-out test samples. C ROC curves of various methods on cross-cohort SCZ prediction. D Select examples of prioritized transcription factors, SNPs, target genes (latent features, and functional links (GRNs, eQTLs) for schizophrenia. Purple: known schizophrenia genes. E Function and pathway enrichments of prioritized schizophrenia SNPs

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