Fig. 4: Generation of the multistructural marker model using the deep learning algorithm.
From: Structural signature of plasma proteins classifies the status of Alzheimer’s disease
a, Eighteen machine learning algorithms were tested. The red bars show algorithms with an accuracy greater than 70%. Deep learning was selected for the highest test set accuracy (83.4%). b, Accuracy was 88.49% on the training set and 83.44% on the test set. The numbers in the boxes indicate individuals classified using the deep-learning-based multi-marker model. c, GFCDTTNLKGLF (C1QA) and SVDCSTNNPSQAKL (CLUS) were commonly selected in ten multi-marker models with an accuracy greater than 70%. d, Accessibility distributions of GFCDTTNLKGLF (C1QA) and SVDCSTNNPSQAKL (CLUS) represented according to group. A two-sided t-test was used. *P < 0.05; ***P < 0.001; ****P < 0.0001. e,f, A multi-marker model was applied for binary classification: healthy versus MCI (AUROC = 0.9343) (e) and MCI versus AD (f) (AUROC = 0.9325). The blue area indicates the 95% CI. Right: the bar graphs show the performance indices. NPV, negative predictive value; PPV, positive predictive value.
