期刊
FEBS LETTERS
卷 593, 期 21, 页码 3029-3039出版社
WILEY
DOI: 10.1002/1873-3468.13536
关键词
antitubercular peptide; feature encoding; linear regression; machine learning; Mycobacterium tuberculosis
资金
- Japan Society for the Promotion of Science [19H04208]
- Ministry of Economy, Trade and Industry, Japan (METI)
- Japan Agency for Medical Research and Development (AMED)
- Grants-in-Aid for Scientific Research [19H04208] Funding Source: KAKEN
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.
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