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. 2020 Feb 26;10:3485. doi: 10.1038/s41598-020-60595-1

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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A framework of the study. The informative genes were selected using a training set by two processes: (Step 1) Extracting DEGs; (Step 2) Selecting informative genes using feature selection methods, including VAE, TF-related genes, hub genes, and the CFG scoring; (Step 3) Learning a prediction model using a training set, and predicting a test set by employing five classification methods, including logistic regression (LR), L1-regularized LR (L1-LR), SVM, RF, and DNN. Stars denote the best performance among five classifying methods. DEG, differentially expressed gene; SAM, significance analysis of microarray; ADNI, Alzheimer’s Disease Neuroimaging Initiative; ANM, AddNeuroMed; VAE, variational autoencoder; TF, transcription factor; PPI, protein-protein interaction; CFG, Convergent Functional Genomics; LR, logistic regression; SVM, support vector machine; RF, random forest; DNN, deep neural network; CN, healthy control; AD, Alzheimer’s disease.