Abstract:
:Secretory proteins of Mycobacterium tuberculosis have created more concern, given their dominant immunogenicity and role in pathogenesis. In view of expensive and time-consuming traditional biochemical experiments, an advanced support vector machine model named SecProMTB is constructed in this study and the proteins are identified by a bioinformatic approach. First, an improved pseudo-amino acid composition (PseAAC) algorithm is used to extract features from all entities. Second, a novel imbalanced-data strategy is proposed and adopted to divide the original data set into train set and test set. Third, to overcome the overfitting problem, feature-ranking algorithms are applied with an increment feature selection. Finally, the model is trained and optimized. Consequently, a model is obtained with an area under the curve of 0.862 and average accuracy of 86% in the independent test. For the convenience of users, SecProMTB and related data are openly accessible at http://server.malab.cn/SecProMTB/index.jsp.
journal_name
Proteomicsjournal_title
Proteomicsauthors
Meng C,Wei L,Zou Qdoi
10.1002/pmic.201900007subject
Has Abstractpub_date
2019-09-01 00:00:00pages
e1900007issue
17eissn
1615-9853issn
1615-9861journal_volume
19pub_type
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