Recent Advancement in Predicting Subcellular Localization of Mycobacterial Protein with Machine Learning Methods

Shi-Hao       Li; Zheng-Xing       Guan; Dan       Zhang; Zi-Mei       Zhang; Jian       Huang; Wuritu       Yang; Hao       Lin
doi:10.2174/1573406415666191004101913
Abstract

Mycobacterium tuberculosis (MTB) can cause the terrible tuberculosis (TB), which is reported as one of the most dreadful epidemics. Although many biochemical molecular drugs have been developed to cope with this disease, the drug resistance—especially the multidrug-resistant (MDR) and extensively drug-resistance (XDR)—poses a huge threat to the treatment. However, traditional biochemical experimental method to tackle TB is time-consuming and costly. Benefited by the appearance of the enormous genomic and proteomic sequence data, TB can be treated via sequence-based biological computational approach-bioinformatics. Studies on predicting subcellular localization of mycobacterial protein (MBP) with high precision and efficiency may help figure out the biological function of these proteins and then provide useful insights for protein function annotation as well as drug design. In this review, we reported the progress that has been made in computational prediction of subcellular localization of MBP including the following aspects: 1) Construction of benchmark datasets. 2) Methods of feature extraction. 3) Techniques of feature selection. 4) Application of several published prediction algorithms. 5) The published results. 6) The further study on prediction of subcellular localization of MBP.
Keywords: Subcellular localization, mycobacterial protein, support vector machine, feature selection, mycobacterium tuberculosis (MTB), terrible tuberculosis (TB).
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DOI https://dx.doi.org/10.2174/1573406415666191004101913	Print ISSN 1573-4064
Publisher Name Bentham Science Publisher	Online ISSN 1875-6638
Medicinal Chemistry

Recent Advancement in Predicting Subcellular Localization of Mycobacterial Protein with Machine Learning Methods

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