Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII
Proteins 87(12), 1011–1020, 23 October 2019 (NIH author manuscript consulted). Publication date and issue month from the PMC author-manuscript header ("Proteins. 2019 Oct 23;87(12):1011–1020"; "Issue date 2019 Dec"). The issue date is a month and stays a month. Submission and acceptance dates are not stated there. PMC's "Published in final edited form" line does not state the kind of the day date. The paper is the CASP organisers' overview of the round.
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- Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII Proteins: Structure, Function, and Bioinformatics (author manuscript in PubMed Central) · published 2019-10-23 · Retrieved for the candidate pack on 2026-10-30 (SHA-256 9ccadedf01cf4ae96d3cceafa8985e512b6d787819383bca3c61da00933923f3). NIH author manuscript (PMC6927249) of Proteins 87(12):1011–1020; the publisher version of record was not retrieved.
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- StatementCASP13: the A7D group from DeepMind had the most accurate results overall, as the organisers state“One group, A7D from DeepMind … appear to have very successfully deployed this technique, and had the most accurate results overall.”section "TEMPLATE FREE MODELING" (PMC author manuscript)Inspect claim & evidence ↗
- StatementCASP13 contact-prediction improvement attributed to deep learning“In CASPs 11 and 12 progress is the result of more sophisticated statistical models, together with largely conventional machine learning. The further jump in CASP13 is the result of the effective deployment of deep learning methods.”discussion of contact-prediction progress across CASP roundsInspect claim & evidence ↗
- StatementCASP13 progress attributed by the organisers to deep learning for inter-residue distances“The most recent experiment (CASP13 held in 2018) saw dramatic progress in structure modeling without use of structural templates”abstractInspect claim & evidence ↗