Once we finalized the model, we downloaded data from the Cancer Genome Atlas () , which provided patient data and their concentration of specific miRNAs. We only included miRNAs in testing that were more commonly expressed in patients who had LC (p < 0.05). We removed all miRNAs that were used in the training of the model. This finalized 29 miRNAs, which were used for independent testing. For each of these 29 miRNAs, we used our developed Python script to generate values for the attributes selected during the attribute selection process. We then input all this data into the previously created HT-based model in WEKA and the associated miRNAs were classified with 82% accuracy and the randomly selected miRNAs had <55% accuracy. The full workflow is visualized in Figure .