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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Binding of the SARS-CoV-2 spike protein to glycans
This article has 10 authors:Reviewed by ScreenIT
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Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions
This article has 7 authors:Reviewed by ScreenIT
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Architecture and self‐assembly of the SARS‐CoV ‐2 nucleocapsid protein
This article has 4 authors:Reviewed by ScreenIT
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Modelling and data-based analysis of COVID-19 outbreak in India : a study on impact of social distancing measures
This article has 3 authors:Reviewed by ScreenIT
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SARS-CoV-2 amino acid substitutions widely spread in the human population are mainly located in highly conserved segments of the structural proteins
This article has 6 authors:Reviewed by ScreenIT
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Computational Study of the Ion and Water Permeation and Transport Mechanisms of the SARS-CoV-2 Pentameric E Protein Channel
This article has 9 authors:Reviewed by ScreenIT
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Identification of five antiviral compounds from the Pandemic Response Box targeting SARS-CoV-2
This article has 5 authors:Reviewed by ScreenIT
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Distinct conformational states of SARS-CoV-2 spike protein
This article has 9 authors:Reviewed by ScreenIT
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Epitope-Based Peptide Vaccine Against Severe Acute Respiratory Syndrome-Coronavirus-2 Nucleocapsid Protein: An in silico Approach
This article has 8 authors:Reviewed by ScreenIT
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Evaluating the determinants of COVID-19 mortality: A cross-country study
This article has 1 author:Reviewed by ScreenIT