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

Abu Sayed Chowdhury,Shira L Broschat,Douglas R Call

doi:10.3390/proceedings2020066007

Abu Sayed Chowdhury, Shira L Broschat + Show 1 more

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https://doi.org/10.3390/proceedings2020066007

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Abstract

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.

Highlights

Thousands of people in the United States die each year due to infections by antimicrobialresistant bacteria [1,2]
Machine learning algorithms are not restricted to sequence similarity, and a machine learning method is a promising alternative for identifying unrecognized antimicrobial-resistance genes (ARGs) in bacteria
We introduce a graphtheoretic feature selection algorithm called symmetrical uncertainty qualitative mutual information (SU-QMI) in which a feature is selected based on estimates of its relevance, nonredundancy, and interdependency

Summary

Introduction

Thousands of people in the United States die each year due to infections by antimicrobialresistant bacteria [1,2]. When new antimicrobial-resistance genes (ARGs) emerge in a population, it may be difficult or impossible to recognize these genes based on conventional sequence similarity algorithms. Sequence matching algorithms such as BLASTp can be applied to find ARGs in bacterial genomes; such algorithms do not work well for dissimilar sequences unless very relaxed. SU-QMI is based on the concepts of symmetrical uncertainty [4], qualitative mutual information [5], and graph theory for predicting AMR in Gram-negative bacteria. The performance of our machine learning model is compared with BLASTp results

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Conclusion