Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins

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 desira...

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Detalles Bibliográficos
Autores principales: Charoenkwan, Phasit, Schaduangrat, Nalini, Hasan, Md Mehedi, Moni, Mohammad Ali, Lió, Pietro, Shoombuatong, Watshara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Leibniz Research Centre for Working Environment and Human Factors 2022
Materias:
Review Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9150013/
https://www.ncbi.nlm.nih.gov/pubmed/35651661
http://dx.doi.org/10.17179/excli2022-4723

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9150013/
https://www.ncbi.nlm.nih.gov/pubmed/35651661
http://dx.doi.org/10.17179/excli2022-4723

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