Fig. 4: Extraction of a gene set suggested to be related to the formation of continuous disease states in NAFLD by applying the SFA to NAFLD disease data.

A Formation of state continuity along the progression of NAFLD. Using the deviation from the normal liver state as the axis, the deviation increases from normal-weight subjects to NAFL and further to NASH, thereby forming a spectrum corresponding to disease progression. B Performance of the constructed autoencoder model. The plot displays, on the vertical axis, the values output by the autoencoder, and on the horizontal axis, the values input to the autoencoder. Pearson’s correlation coefficient (r) was calculated based on the input and output data values. In addition, the green dotted line represents y = x. C Test data accuracy for the GC and SFC constructed based on NAFLD latent-space data is shown for each model set. Model sets indicated in red represent those with a test data accuracy that is more than twice that of random classification (random classification test accuracy: 33%). D Diagram showing the extraction process of feature 10. The “importance” in the left table indicates the difference in feature importances between the SFC and GC. For model sets 4 and 5, the common latent-space feature assigned greater importance by the SFC than by the GC was identified based on the rank sum. E Enrichment analysis based on the DisGeNET database for the gene set obtained from feature 10. The top 20 terms, sorted in ascending order based on p-values, are shown. F Enrichment analysis based on the DisGeNET database for the gene set obtained from feature 11, which is a control feature. G Enrichment analysis based on the TRRUST database for the gene set obtained from feature 10.