ArticleProteins2022
In silico prediction of Severe Acute Respiratory Syndrome Coronavirus 2 main protease cleavage sites.
Article in Proteins, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the emerging subjects to combat the SARS-CoV-2 virus is to design accurate and efficient drug such as inhibitors against the viral protease to stop the viral spread. In addition to laboratory investigation of the viral protease, which is fundamental, the in silico research of viral protease such as the protease cleavage site prediction is critically important and urgent. However, this problem has yet to be addressed. This article has, for the first time, investigated this problem using the pattern recognition approaches. The article has shown that the pattern recognition approaches incorporating a specially tailored kernel function for dealing with amino acids has the outstanding performance in the accuracy of cleavage site prediction and the discovery of the prototype cleavage peptides.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.