-
Ensemble-amppred: Robust amp prediction and recognition using the ensemble learning method with a new hybrid feature for differentiating amps
- Back
Document Title
Ensemble-amppred: Robust amp prediction and recognition using the ensemble learning method with a new hybrid feature for differentiating amps
Author
Lertampaiporn S., Vorapreeda T., Hongsthong A., Thammarongtham C.
Name from Authors Collection
Scopus Author ID
54880593800
Affiliations
National Center for Genetic Engineering and Biotechnology, Biochemical Engineering and Systems Biology Research Group, National Science and Technology Development Agency, King Mongkut’s University of Technology Thonburi, Khun Thian, Bangkok, 10150, Thailand
Type
Article
Source Title
Genes
ISSN
20734425
Year
2021
Volume
12
Issue
2
Page
42736
Open Access
All Open Access, Gold
Publisher
MDPI AG
DOI
10.3390/genes12020137
Format
Abstract
Antimicrobial peptides (AMPs) are natural peptides possessing antimicrobial activities. These peptides are important components of the innate immune system. They are found in various organisms. AMP screening and identification by experimental techniques are laborious and time-consuming tasks. Alternatively, computational methods based on machine learning have been developed to screen potential AMP candidates prior to experimental verification. Although various AMP prediction programs are available, there is still a need for improvement to reduce false positives (FPs) and to increase the predictive accuracy. In this work, several well-known single and ensemble machine learning approaches have been explored and evaluated based on balanced training datasets and two large testing datasets. We have demonstrated that the developed program with various predictive models has high performance in differentiating between AMPs and non-AMPs. Thus, we describe the development of a program for the prediction and recognition of AMPs using MaxProbVote, which is an ensemble model. Moreover, to increase prediction efficiency, the ensemble model was integrated with a new hybrid feature based on logistic regression. The ensemble model integrated with the hybrid feature can effectively increase the prediction sensitivity of the developed program called Ensemble-AMPPred, resulting in overall improvements in terms of both sensitivity and specificity compared to those of currently available programs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Industrial Classification
Knowledge Taxonomy Level 1
Knowledge Taxonomy Level 2
Knowledge Taxonomy Level 3
Funding Sponsor
National Science and Technology Development Agency; National Center for Genetic Engineering and Biotechnology
License
N/A
Rights
N/A
Publication Source
Scopus
Note
Full text
Document
-
Ensembleamppred-Robust-amp-prediction-and-recognition-using-the-ensemble-learning-method-with-a-new-hybrid-feature-for-differentiatiDownload