SU-QMI: A Feature Selection Method Based on Graph Theory for Prediction of Antimicrobial Resistance in Gram-Negative Bacteria

Machine learning can be used as an alternative to similarity algorithms such as BLASTp when the latter fail to identify dissimilar antimicrobial-resistance genes (ARGs) in bacteria; however, determining the most informative characteristics, known as features, for antimicrobial resistance (AMR) is essential to obtain accurate predictions. In this paper, we introduce a feature selection algorithm called symmetrical uncertainty qualitative mutual information (SU-QMI), which selects features based on estimates of their relevance, redundancy, and interdependency. We use these together with graph theory to derive a feature selection method for identifying putative ARGs in Gram-negative bacteria. We extract physicochemical, evolutionary, and structural features from the protein sequences of five genera of Gram-negative bacteria—Acinetobacter, Klebsiella, Campylobacter, Salmonella, and Escherichia—which confer resistance to acetyltransferase (aac), β-lactamase (bla), and dihydrofolate reductase (dfr). Our SU-QMI algorithm is then used to find the best subset of features, and a support vector machine (SVM) model is trained for AMR prediction using this feature subset. We evaluate performance using an independent set of protein sequences from three Gram-negative bacterial genera—Pseudomonas, Vibrio, and Enterobacter—and achieve prediction accuracy ranging from 88 to 100%. Compared to the SU-QMI method, BLASTp requires similarity as low as 53% for comparable classification results. Our results indicate the effectiveness of the SU-QMI method for selecting the best protein features for AMR prediction in Gram-negative bacteria.