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
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Lung Disease Network Reveals the Impact of Comorbidity on SARS-CoV-2 infection
This article has 1 author:Reviewed by ScreenIT
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A Computational Toolset for Rapid Identification of SARS-CoV-2, other Viruses, and Microorganisms from Sequencing Data
This article has 5 authors:Reviewed by ScreenIT
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Functional prediction and comparative population analysis of variants in genes for proteases and innate immunity related to SARS-CoV-2 infection
This article has 7 authors:Reviewed by ScreenIT
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COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images
This article has 3 authors:Reviewed by ScreenIT
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Structure of SARS-CoV-2 main protease in the apo state reveals the inactive conformation
This article has 15 authors:Reviewed by ScreenIT
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Immune complement and coagulation dysfunction in adverse outcomes of SARS-CoV-2 infection
This article has 12 authors:Reviewed by ScreenIT
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Rapid response flow cytometric assay for the detection of antibody responses to SARS-CoV-2
This article has 16 authors:Reviewed by ScreenIT
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Genome Analysis of SARS-CoV-2 Isolate from Bangladesh
This article has 4 authors:Reviewed by ScreenIT
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CoV3D: a database of high resolution coronavirus protein structures
This article has 6 authors:Reviewed by ScreenIT
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Evolving Epidemiology and Effect of Non-pharmaceutical Interventions on the Epidemic of Coronavirus Disease 2019 in Shenzhen, China
This article has 19 authors:Reviewed by ScreenIT