ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
-
SARS-CoV-2 Encodes a PPxY Late Domain Motif Known to Enhance Budding and Spread in Enveloped RNA Viruses
This article has 1 author:Reviewed by ScreenIT
-
Bioinformatics Study on Structural Proteins of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) For Better Understanding the Vaccine Development
This article has 2 authors:Reviewed by ScreenIT
-
Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19
This article has 3 authors:Reviewed by ScreenIT
-
Inhibition of PIKfyve kinase prevents infection by Zaire ebolavirus and SARS-CoV-2
This article has 11 authors:Reviewed by ScreenIT
-
Elucidating the differences in the molecular mechanism of receptor binding between 2019-nCoV and the SARS-CoV viruses using computational tools
This article has 5 authors:Reviewed by ScreenIT
-
Human iPSC-Derived Cardiomyocytes are Susceptible to SARS-CoV-2 Infection
This article has 4 authors:Reviewed by ScreenIT
-
A New Resource for Genomics and Precision Health Information and Publications on the Investigation and Control of COVID-19 and other Coronaviruses
This article has 3 authors:Reviewed by ScreenIT
-
Cross-reactive neutralization of SARS-CoV-2 by serum antibodies from recovered SARS patients and immunized animals
This article has 9 authors:Reviewed by ScreenIT
-
Enisamium is a small molecule inhibitor of the influenza A virus and SARS-CoV-2 RNA polymerases
This article has 7 authors:Reviewed by ScreenIT
-
Sex, androgens and regulation of pulmonary AR, TMPRSS2 and ACE2
This article has 12 authors:Reviewed by ScreenIT