Prediction of Antimicrobial Peptides Based on Sequence Alignment and Support Vector Machine-Pairwise Algorithm Utilizing LZ-Complexity
Joint Authors
Ng, Xin Yi
Rosdi, Bakhtiar Affendi
Shahrudin, Shahriza
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-02-23
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs) which are important to the immune system.
Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics.
However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time.
Therefore, a computational tool for AMPs prediction is needed to resolve this problem.
In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm.
It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used.
Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity.
American Psychological Association (APA)
Ng, Xin Yi& Rosdi, Bakhtiar Affendi& Shahrudin, Shahriza. 2015. Prediction of Antimicrobial Peptides Based on Sequence Alignment and Support Vector Machine-Pairwise Algorithm Utilizing LZ-Complexity. BioMed Research International،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1054648
Modern Language Association (MLA)
Ng, Xin Yi…[et al.]. Prediction of Antimicrobial Peptides Based on Sequence Alignment and Support Vector Machine-Pairwise Algorithm Utilizing LZ-Complexity. BioMed Research International No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1054648
American Medical Association (AMA)
Ng, Xin Yi& Rosdi, Bakhtiar Affendi& Shahrudin, Shahriza. Prediction of Antimicrobial Peptides Based on Sequence Alignment and Support Vector Machine-Pairwise Algorithm Utilizing LZ-Complexity. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1054648
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1054648