Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/72378
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPhasit Charoenkwanen_US
dc.contributor.authorNalini Schaduangraten_US
dc.contributor.authorMd Mehedi Hasanen_US
dc.contributor.authorMohammad Ali Monien_US
dc.contributor.authorPietro Lióen_US
dc.contributor.authorWatshara Shoombuatongen_US
dc.date.accessioned2022-05-27T08:25:18Z-
dc.date.available2022-05-27T08:25:18Z-
dc.date.issued2022-01-03en_US
dc.identifier.issn16112156en_US
dc.identifier.other2-s2.0-85126034176en_US
dc.identifier.other10.17179/excli2022-4723en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85126034176&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/72378-
dc.description.abstractThermophilic 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.en_US
dc.subjectAgricultural and Biological Sciencesen_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectPharmacology, Toxicology and Pharmaceuticsen_US
dc.titleEMPIRICAL COMPARISON AND ANALYSIS OF MACHINE LEARNING-BASED PREDICTORS FOR PREDICTING AND ANALYZING OF THERMOPHILIC PROTEINSen_US
dc.typeJournalen_US
article.title.sourcetitleEXCLI Journalen_US
article.volume21en_US
article.stream.affiliationsDepartment of Computer Science and Technologyen_US
article.stream.affiliationsThe University of Queenslanden_US
article.stream.affiliationsTulane University School of Medicineen_US
article.stream.affiliationsMahidol Universityen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.


Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.