With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called ‘features’ in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics bacitracin and vancomycin. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies.

随着全基因组序列可用性的不断提高，机器学习方法可以替代传统的基于比对的方法来鉴定新的抗药性基因。当病原体无法在实验室中培养时，这种方法特别有用。在先前的工作中，我们提出了一种基于博弈论的特征评估算法。当使用通过该算法识别的蛋白质特征（在机器学习中称为“特征”）时，我们的模型可以准确地识别革兰氏阴性细菌中的抗菌素耐药性（AMR）基因。在这里，我们将研究扩展到革兰氏阳性细菌，结果表明，将博弈论识别的特征与机器学习相结合，可以实现编码细菌杆菌肽耐药性的基因的分类精度在87％至90％之间和万古霉素。重要的是，我们提供了一个独立的软件工具，该工具可实现这些研究中使用的博弈论算法和机器学习模型。