Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.
Biomedical Informatics R&D Center, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.
FEBS Lett. 2019 Nov;593(21):3029-3039. doi: 10.1002/1873-3468.13536. Epub 2019 Jul 23.
Tuberculosis (TB) is a leading killer caused by Mycobacterium tuberculosis. Recently, anti-TB peptides have provided an alternative approach to combat antibiotic tolerance. We have developed an effective computational predictor, identification of antitubercular peptides (iAntiTB), by the integration of multiple feature vectors deriving from the amino acid sequences via random forest (RF) and support vector machine (SVM) classifiers. The iAntiTB combines the RF and SVM scores via linear regression to enhance the prediction accuracy. To make a robust and accurate predictor, we prepared the two datasets with different types of negative samples. The iAntiTB achieved area under the ROC curve values of 0.896 and 0.946 on the training datasets of the first and second datasets, respectively. The iAntiTB outperformed the other existing predictors.
结核病（TB）是由结核分枝杆菌引起的主要杀手。最近，抗结核肽为对抗抗生素耐药性提供了一种替代方法。我们通过随机森林（RF）和支持向量机（SVM）分类器从氨基酸序列中提取多个特征向量，开发了一种有效的计算预测器，即抗结核肽识别（iAntiTB）。iAntiTB 通过线性回归结合 RF 和 SVM 分数，以提高预测准确性。为了构建一个稳健而准确的预测器，我们使用两种不同类型的阴性样本准备了两个数据集。iAntiTB 在第一个和第二个数据集的训练数据集上的 ROC 曲线下面积分别为 0.896 和 0.946。iAntiTB 优于其他现有的预测器。
