Figure 4

The flowchart of SV-HMM showing the stepwise procedure. The above figure shows the stepwise procedure we have performed. (1) data collection, building gp120 benchmark dataset and pre-processing datasets; (2) structural gp120 profile construction including matching up and calculating the 3D distance between every glycan of the query and template models. (3) the information obtained in (2) and (3) were combined and normalised to fall in the interval [--1, 1] to be fed into networks; (4) target levels were assigned to each profile (positive, +1, for RPs, 0 for SP. –1 for LTNP); (5) a hold-out method, to divide the combined dataset into ten subsets (training and testing sets); (6) model training on each set, to create a model; (7) simulation of each model on the test set, to obtain predicted outputs; and (8) post-processing to find predicted HIV progressor groups. The procedure from (6) to (8) was performed iteratively until we obtained the most suitable kernel and the optimal hyperparameters for SV-HMM for gp120 benchmark dataset.