Using machine learning to predict and analyze complex trait diseases: Lessons from a simple abstract model
Fig 14
The results of running a t-SNE algorithm with NN weights of a “Subtype” model as input (500% of the original size, two subtypes model, AUC of 0.91).
Predicting disease status in a subtype model was shown to be a difficult task. Still the plot shows a reasonable separation between pathways that belong to type 1 (on the upper part) and pathways that belong to subtype 2 (mostly lower part).