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Abstract：
BACKGROUND: Mutations in an enzyme target are one of the most common mechanisms whereby antibiotic resistance arises. Identification of the resistance mutations in bacteria is essential for understanding the structural basis of antibiotic resistance and design of new drugs. However, the traditionally used experimental approaches to identify resistance mutations were usually labor-intensive and costly.
RESULTS: We present a machine learning (ML)-based classifier for predicting rifampicin (Rif) resistance mutations in bacterial RNA Polymerase subunit β (RpoB). A total of 186 mutations were gathered from the literature for developing the classifier, using 80% of the data as the training set and the rest as the test set. The features of the mutated RpoB and their binding energies with Rif were calculated through computational methods, and used as the mutation attributes for modeling. Classifiers based on five ML algorithms, i.e. decision tree, k nearest neighbors, naïve Bayes, probabilistic neural network and support vector machine, were first built, and a majority consensus (MC) approach was then used to obtain a new classifier based on the classifications of the five individual ML algorithms. The MC classifier comprehensively improved the predictive performance, with accuracy, F-measure and AUC of 0.78, 0.83 and 0.81for training set whilst 0.84, 0.87 and 0.83 for test set, respectively.
CONCLUSIONS: The MC classifier provides an alternative methodology for rapid identification of resistance mutations in bacteria, which may help with early detection of antibiotic resistance and new drug discovery.
RESULTS: We present a machine learning (ML)-based classifier for predicting rifampicin (Rif) resistance mutations in bacterial RNA Polymerase subunit β (RpoB). A total of 186 mutations were gathered from the literature for developing the classifier, using 80% of the data as the training set and the rest as the test set. The features of the mutated RpoB and their binding energies with Rif were calculated through computational methods, and used as the mutation attributes for modeling. Classifiers based on five ML algorithms, i.e. decision tree, k nearest neighbors, naïve Bayes, probabilistic neural network and support vector machine, were first built, and a majority consensus (MC) approach was then used to obtain a new classifier based on the classifications of the five individual ML algorithms. The MC classifier comprehensively improved the predictive performance, with accuracy, F-measure and AUC of 0.78, 0.83 and 0.81for training set whilst 0.84, 0.87 and 0.83 for test set, respectively.
CONCLUSIONS: The MC classifier provides an alternative methodology for rapid identification of resistance mutations in bacteria, which may help with early detection of antibiotic resistance and new drug discovery.
摘要：
背景:酶靶标中的突变是产生抗生素抗性的最常见机制之一。细菌中抗性突变的鉴定对于理解抗生素抗性的结构基础和新药的设计至关重要。然而,传统上使用的鉴定抗性突变的实验方法通常是劳动密集型且昂贵的。
结果：我们提出了一种基于机器学习（ML）的分类器，用于预测细菌RNA聚合酶亚基β（RpoB）中的利福平（Rif）抗性突变。从文献中收集了总共186个突变用于开发分类器，使用80%的数据作为训练集，其余的作为测试集。通过计算方法计算了突变的RpoB的特征及其与Rif的结合能，并用作建模的突变属性。基于五种ML算法的分类器，即决策树，k最近的邻居，天真贝叶斯，概率神经网络和支持向量机，首先建造，然后使用多数共识（MC）方法基于五个单独的ML算法的分类来获得新的分类器。MC分类器全面提高了预测性能，准确地说，训练集的F测量和AUC为0.78、0.83和0.81，而测试集的AUC为0.84、0.87和0.83，分别。
结论：MC分类器提供了一种快速鉴定细菌耐药突变的替代方法，这可能有助于早期发现抗生素耐药性和发现新药。
结果：我们提出了一种基于机器学习（ML）的分类器，用于预测细菌RNA聚合酶亚基β（RpoB）中的利福平（Rif）抗性突变。从文献中收集了总共186个突变用于开发分类器，使用80%的数据作为训练集，其余的作为测试集。通过计算方法计算了突变的RpoB的特征及其与Rif的结合能，并用作建模的突变属性。基于五种ML算法的分类器，即决策树，k最近的邻居，天真贝叶斯，概率神经网络和支持向量机，首先建造，然后使用多数共识（MC）方法基于五个单独的ML算法的分类来获得新的分类器。MC分类器全面提高了预测性能，准确地说，训练集的F测量和AUC为0.78、0.83和0.81，而测试集的AUC为0.84、0.87和0.83，分别。
结论：MC分类器提供了一种快速鉴定细菌耐药突变的替代方法，这可能有助于早期发现抗生素耐药性和发现新药。
