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
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Evolutionary dynamics and geographic dispersal of beta coronaviruses in African bats
This article has 3 authors: -
ACE2 Interaction Networks in COVID-19: A Physiological Framework for Prediction of Outcome in Patients with Cardiovascular Risk Factors
This article has 8 authors:Reviewed by ScreenIT
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ACE2‐Variants Indicate Potential SARS‐CoV‐2‐Susceptibility in Animals: A Molecular Dynamics Study
This article has 6 authors:Reviewed by ScreenIT
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Crystal structures of SARS-CoV-2 ADP-ribose phosphatase: from the apo form to ligand complexes
This article has 7 authors:Reviewed by ScreenIT
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Characteristic and quantifiable COVID-19-like abnormalities in CT- and PET/CT-imaged lungs of SARS-CoV-2-infected crab-eating macaques ( Macaca fascicularis )
This article has 33 authors:Reviewed by ScreenIT
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Detection of viral RNA fragments in human iPSC cardiomyocytes following treatment with extracellular vesicles from SARS-CoV-2 coding sequence overexpressing lung epithelial cells
This article has 10 authors:Reviewed by ScreenIT
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Growth Factor Receptor Signaling Inhibition Prevents SARS-CoV-2 Replication
This article has 6 authors:Reviewed by ScreenIT
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Insights into molecular evolution recombination of pandemic SARS-CoV-2 using Saudi Arabian sequences
This article has 5 authors:Reviewed by ScreenIT
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Estimating and forecasting COVID-19 attack rates and mortality
This article has 5 authors:Reviewed by ScreenIT
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Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome
This article has 4 authors:Reviewed by ScreenIT