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  1. Deep learning predicts tissue outcomes in retinal organoids

    This article has 12 authors:
    1. Cassian Afting
    2. Norin Bhatti
    3. Christina Schlagheck
    4. Encarnación Sánchez Salvador
    5. Laura Herrera-Astorga
    6. Rashi Agarwal
    7. Risa Suzuki
    8. Nicolaj Hackert
    9. Hanns-Martin Lorenz
    10. Lucie Zilova
    11. Joachim Wittbrodt
    12. Tarik Exner

    Reviewed by Review Commons

    This article has 4 evaluationsAppears in 1 listLatest version Latest activity
  2. THE FAM53C/DYRK1A axis regulates the G1/S transition of the cell cycle

    This article has 15 authors:
    1. Taylar Hammond
    2. Jong Bin Choi
    3. Miles W Membreño
    4. Janos Demeter
    5. Roy Ng
    6. Debadrita Bhattacharya
    7. Thuyen N Nguyen
    8. Griffin G Hartmann
    9. Caterina I Colón
    10. Carine Bossard
    11. Jan M Skotheim
    12. Peter K Jackson
    13. Anca Pasca
    14. Seth M Rubin
    15. Julien Sage
    This article has been curated by 1 group:
    • Curated by eLife

      eLife Assessment

      This study identifies the uncharacterised protein FAM53C as a novel, potential regulator of the G1/S cell cycle transition, linking its function to the DYRK1A kinase and the RB/p53 pathways. The work is valuable and of interest to the cell cycle field, leveraging a strong computational screen to identify a new candidate. The findings are solid, although confidence in the siRNA depletion phenotypes would have been higher with rescue experiments using an siRNA-resistant cDNA and more robust quantification of some immunoassay data.

      [Editors' note: this paper was reviewed by Review Commons.]

    Reviewed by eLife, Review Commons

    This article has 9 evaluationsAppears in 2 listsLatest version Latest activity