SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins
Issued Date
2022-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85126076923
Pubmed ID
35260777
Journal Title
Scientific Reports
Volume
12
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.12 No.1 (2022)
Suggested Citation
Ahmad S., Charoenkwan P., Quinn J.M.W., Moni M.A., Hasan M.M., Lio’ P., Shoombuatong W. SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins. Scientific Reports Vol.12 No.1 (2022). doi:10.1038/s41598-022-08173-5 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/86440
Title
SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins
Other Contributor(s)
Abstract
Fast and accurate identification of phage virion proteins (PVPs) would greatly aid facilitation of antibacterial drug discovery and development. Although, several research efforts based on machine learning (ML) methods have been made for in silico identification of PVPs, these methods have certain limitations. Therefore, in this study, we propose a new computational approach, termed SCORPION, (StaCking-based Predictior fOR Phage VIrion PrOteiNs), to accurately identify PVPs using only protein primary sequences. Specifically, we explored comprehensive 13 different feature descriptors from different aspects (i.e., compositional information, composition-transition-distribution information, position-specific information and physicochemical properties) with 10 popular ML algorithms to construct a pool of optimal baseline models. These optimal baseline models were then used to generate probabilistic features (PFs) and considered as a new feature vector. Finally, we utilized a two-step feature selection strategy to determine the optimal PF feature vector and used this feature vector to develop a stacked model (SCORPION). Both tenfold cross-validation and independent test results indicate that SCORPION achieves superior predictive performance than its constitute baseline models and existing methods. We anticipate SCORPION will serve as a useful tool for the cost-effective and large-scale screening of new PVPs. The source codes and datasets for this work are available for downloading in the GitHub repository (https://github.com/saeed344/SCORPION).