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Kryshtafovych et al. (Proteins, 23 October 2019), reporting on CASP13 as its organisers, state that the experiment held in 2018 saw dramatic progress in structure modeling without use of structural templates, and that progress was driven by the successful application of deep learning techniques to predict inter-residue distances. Copy claim link
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Provision Statement · provision ID mod-casp13-deep-learning Date 2019-10-23 · publication Records dated 2019 → Scope and limits The CASP organisers' summary in their overview of the round (the independent assessors' analyses are separate papers), in the author-manuscript expression. It attributes progress to a class of methods, not to any one group, and it is not a measurement reproduced here.
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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) · 2019-10-23
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Source excerpt “The most recent experiment (CASP13 held in 2018) saw dramatic progress in structure modeling without use of structural templates” Location in source abstract
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-30
Source details and check notes Authors Andriy Kryshtafovych; Torsten Schwede; Maya Topf; Krzysztof Fidelis; John Moult Version 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. Canonical address Open the canonical source ↗ Accessed 2026-10-30 Language English (en) Attribution Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, John Moult, “Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII”, 2019-10-23. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, AI in biology (research/ai-in-biology). The quotation (19 words) matched the text retrieved for the candidate pack on 2026-10-30. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. No human editorial review. primary source Supports the claim
Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII ↗ Proteins: Structure, Function, and Bioinformatics (author manuscript in PubMed Central) · 2019-10-23
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Cite the original source Cite the original document, not Selekoa. Check these details against it.
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Source excerpt “Progress was driven by the successful application of deep learning techniques to predict inter-residue distances.” Location in source abstract
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-30
Source details and check notes Authors Andriy Kryshtafovych; Torsten Schwede; Maya Topf; Krzysztof Fidelis; John Moult Version 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. Canonical address Open the canonical source ↗ Accessed 2026-10-30 Language English (en) Attribution Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, John Moult, “Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII”, 2019-10-23. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, AI in biology (research/ai-in-biology). The quotation (15 words) matched the text retrieved for the candidate pack on 2026-10-30. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. No human editorial review. Status and meaning Editorial status. Not reviewed by an editor . An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.
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Date. Date given in PMC's "Published in final edited form" line for the organisers' overview paper (kind not stated). CASP13 itself was held in 2018 as the paper states.
Record ID aib-mod-casp13-deep-learning