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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TMPRSS2, a SARS-CoV-2 internalization protease is downregulated in head and neck cancer patients
This article has 17 authors:Reviewed by ScreenIT
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Massive transient damage of the olfactory epithelium associated with infection of sustentacular cells by SARS-CoV-2 in golden Syrian hamsters
This article has 16 authors:Reviewed by ScreenIT
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Oral drug repositioning candidates and synergistic remdesivir combinations for the prophylaxis and treatment of COVID-19
This article has 15 authors:Reviewed by ScreenIT
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Lack of susceptibility of poultry to SARS-CoV-2 and MERS-CoV
This article has 6 authors:Reviewed by ScreenIT
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The Integrin Binding Peptide, ATN-161, as a Novel Therapy for SARS-CoV-2 Infection
This article has 8 authors:Reviewed by ScreenIT
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Machine Learning Models Identify Inhibitors of SARS-CoV-2
This article has 17 authors:Reviewed by ScreenIT
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Antigenic evolution on global scale reveals potential natural selection of SARS-CoV-2 by pre-existing cross-reactive T cell immunity
This article has 5 authors:Reviewed by ScreenIT
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Targeting ACE2-RBD interaction as a platform for COVID19 therapeutics: Development and drug repurposing screen of an AlphaLISA proximity assay
This article has 7 authors:Reviewed by ScreenIT
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AGE IS ASSOCIATED WITH INCREASED EXPRESSION OF PATTERN RECOGNITION RECEPTOR GENES AND ACE2 , THE RECEPTOR FOR SARS-COV-2: IMPLICATIONS FOR THE EPIDEMIOLOGY OF COVID-19 DISEASE
This article has 12 authors:Reviewed by ScreenIT
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Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases
This article has 6 authors:Reviewed by ScreenIT