Once we had a final list of miRNAs and their attributes, we input our data into Waikato Environment for Knowledge Analysis (WEKA) . Since we had many attributes and many of them may add noise to the model, we used WEKA's InfoGainAttributeEval feature to perform attribute selection on the dataset. We used the Ranker functionality to choose the attributes that were most significant in classification of the data. WEKA's attribute selection narrowed our list of attributes from 876 attributes to the 25 most important for classification. The selected target pathways are shown in Table . These target pathways are more common in the miRNAs that were used in training our model and already have known association with LC. We also analyzed the features chosen from the attribute selection. For the predicted target genes and pathway processes, we visualized the relation of the different target genes in more complex cancer pathways using KEGG () .