EMPIRICAL COMPARISON AND ANALYSIS OF MACHINE LEARNING-BASED PREDICTORS FOR PREDICTING AND ANALYZING OF THERMOPHILIC PROTEINS
Issued Date
2022-01-03
Resource Type
eISSN
16112156
Scopus ID
2-s2.0-85126034176
Journal Title
EXCLI Journal
Volume
21
Start Page
554
End Page
570
Rights Holder(s)
SCOPUS
Bibliographic Citation
EXCLI Journal Vol.21 (2022) , 554-570
Suggested Citation
Charoenkwan P., Schaduangrat N., Hasan M.M., Moni M.A., Lió P., Shoombuatong W. EMPIRICAL COMPARISON AND ANALYSIS OF MACHINE LEARNING-BASED PREDICTORS FOR PREDICTING AND ANALYZING OF THERMOPHILIC PROTEINS. EXCLI Journal Vol.21 (2022) , 554-570. 570. doi:10.17179/excli2022-4723 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/83863
Title
EMPIRICAL COMPARISON AND ANALYSIS OF MACHINE LEARNING-BASED PREDICTORS FOR PREDICTING AND ANALYZING OF THERMOPHILIC PROTEINS
Other Contributor(s)
Abstract
Thermophilic proteins (TPPs) are critical for basic research and in the food industry due to their ability to maintain a thermodynamically stable fold at extremely high temperatures. Thus, the expeditious identification of novel TPPs through computational models from protein sequences is very desirable. Over the last few decades, a number of computational methods, especially machine learning (ML)-based methods, for in silico prediction of TPPs have been developed. Therefore, it is desirable to revisit these methods and summarize their advantages and disadvan-tages in order to further develop new computational approaches to achieve more accurate and improved prediction of TPPs. With this goal in mind, we comprehensively investigate a large collection of fourteen state-of-the-art TPP predictors in terms of their dataset size, feature encoding schemes, feature selection strategies, ML algorithms, evaluation strategies and web server/software usability. To the best of our knowledge, this article represents the first comprehensive review on the development of ML-based methods for in silico prediction of TPPs. Among these TPP predictors, they can be classified into two groups according to the interpretability of ML algorithms employed (i.e., computational black-box methods and computational white-box methods). In order to perform the comparative analysis, we conducted a comparative study on several currently available TPP predictors based on two benchmark datasets. Finally, we provide future perspectives for the design and development of new computational models for TPP prediction. We hope that this comprehensive review will facilitate researchers in selecting an appropriate TPP predictor that is the most suitable one to deal with their purposes and provide useful perspectives for the development of more effective and accurate TPP predictors.