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.

About Followers

Latest preprint reviews

  1. Interleukin‐6 in Covid‐19: A systematic review and meta‐analysis

    1. Eric A. Coomes
    2. Hourmazd Haghbayan

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  2. A flexible method for optimising sharing of healthcare resources and demand in the context of the COVID-19 pandemic

    1. Lucas Lacasa
    2. Robert Challen
    3. Ellen Brooks-Pollock
    4. Leon Danon

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  3. COVID-19 infection during pregnancy: a systematic review to summarize possible symptoms, treatments, and pregnancy outcomes

    1. Md. Mostaured Ali Khan
    2. Md. Nuruzzaman Khan
    3. Md. Golam Mustagir
    4. Juwel Rana
    5. Md. Rajwanul Haque
    6. Md. Mosfequr Rahman

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  4. Insights into The Codon Usage Bias of 13 Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Isolates from Different Geo-locations

    1. Saif M. Khodary
    2. Ali Mostafa Anwar

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  5. deepMINE - Natural Language Processing based Automatic Literature Mining and Research Summarization for Early-Stage Comprehension in Pandemic Situations specifically for COVID-19

    1. Bhrugesh Joshi
    2. Vishvajit Bakarola
    3. Parth Shah
    4. Ramar Krishnamurthy

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  6. Identification of SARS-CoV-2 Vaccine Epitopes Predicted to Induce Long-Term Population-Scale Immunity

    1. Mark Yarmarkovich
    2. John M. Warrington
    3. Alvin Farrel
    4. John M. Maris

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  7. SARS-CoV-2 Receptors and Entry Genes Are Expressed in the Human Olfactory Neuroepithelium and Brain

    1. Leon Fodoulian
    2. Joël Tuberosa
    3. Daniel Rossier
    4. Madlaina Boillat
    5. Chenda Kan
    6. Véronique Pauli
    7. Kristof Egervari
    8. Johannes A. Lobrinus
    9. Basile N. Landis
    10. Alan Carleton
    11. Ivan Rodriguez

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  8. Identification of Common Deletions in the Spike Protein of Severe Acute Respiratory Syndrome Coronavirus 2

    1. Zhe Liu
    2. Huanying Zheng
    3. Huifang Lin
    4. Mingyue Li
    5. Runyu Yuan
    6. Jinju Peng
    7. Qianling Xiong
    8. Jiufeng Sun
    9. Baisheng Li
    10. Jie Wu
    11. Lina Yi
    12. Xiaofang Peng
    13. Huan Zhang
    14. Wei Zhang
    15. Ruben J. G. Hulswit
    16. Nick Loman
    17. Andrew Rambaut
    18. Changwen Ke
    19. Thomas A. Bowden
    20. Oliver G. Pybus
    21. Jing Lu

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  9. Comparative genomics suggests limited variability and similar evolutionary patterns between major clades of SARS-CoV-2

    1. Matteo Chiara
    2. David S. Horner
    3. Carmela Gissi
    4. Graziano Pesole

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  10. Molecular Basis for ADP-Ribose Binding to the Mac1 Domain of SARS-CoV-2 nsp3

    1. David N. Frick
    2. Rajdeep S. Virdi
    3. Nemanja Vuksanovic
    4. Narayan Dahal
    5. Nicholas R. Silvaggi

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity
Newer Page 376 of 2361 Older