BenchStab: a tool for automated querying of web-based stability predictors
Bioinformatics, ISSN: 1367-4811, Vol: 40, Issue: 9
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Summary: Protein design requires information about how mutations affect protein stability. Many web-based predictors are available for this purpose, yet comparing them or using them en masse is difficult. Here, we present BenchStab, a console tool/Python package for easy and quick execution of 19 predictors and result collection on a list of mutants. Moreover, the tool is easily extensible with additional predictors. We created an independent dataset derived from the FireProtDB and evaluated 24 different prediction methods.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85205274889&origin=inward; http://dx.doi.org/10.1093/bioinformatics/btae553; http://www.ncbi.nlm.nih.gov/pubmed/39259175; https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btae553/7755040; https://dx.doi.org/10.1093/bioinformatics/btae553; https://academic.oup.com/bioinformatics/article/40/9/btae553/7755040
Oxford University Press (OUP)
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