SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins
Issued Date
2022-07-01
Resource Type
ISSN
00104825
eISSN
18790534
Scopus ID
2-s2.0-85131801539
Pubmed ID
35690478
Journal Title
Computers in Biology and Medicine
Volume
146
Rights Holder(s)
SCOPUS
Bibliographic Citation
Computers in Biology and Medicine Vol.146 (2022)
Suggested Citation
Charoenkwan P., Schaduangrat N., Moni M.A., Lio’ P., Manavalan B., Shoombuatong W. SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins. Computers in Biology and Medicine Vol.146 (2022). doi:10.1016/j.compbiomed.2022.105704 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84270
Title
SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins
Other Contributor(s)
Abstract
Thermophilic proteins (TPPs) are important in the field of protein biochemistry and development of new enzymes. Thus, computational methods must be urgently developed to accurately and rapidly identify TPPs. To date, several computational methods have been developed for TPP identification; however, few limitations in terms of performance and utility remain. In this study, we present a novel computational method, SAPPHIRE, to achieve more accurate identification of TPPs using only sequence information without any need for structural information. We combined twelve different feature encodings representing different perspectives and six popular machine learning algorithms to train 72 baseline models and extract the key information of TPPs. Subsequently, the informative predicted probabilities from the baseline models were mined and selected using a genetic algorithm in conjunction with a self-assessment-report approach. Finally, the final meta-predictor, SAPPHIRE, was built and optimized by applying an optimal feature set. The performance of SAPPHIRE in the 10-fold cross-validation test showed that a superior predictive performance compared with several baseline models could be achieved. Moreover, SAPPHIRE yielded an accuracy of 0.942 and Matthew's coefficient correlation of 0.884, which were 7.68 and 5.12% higher than those of the current existing methods, respectively, as indicated by the independent test. The proposed computational approach is anticipated to facilitate large-scale identification of TPPs and accelerate their applications in the food industry. The codes and datasets are available at https://github.com/plenoi/SAPPHIRE.