Claim · 2019-10-23 · publication · Stated in source · Supported

Kryshtafovych et al. (Proteins, 23 October 2019) state that the further jump in CASP13 contact-prediction performance is the result of the effective deployment of deep learning methods, where the gains in CASP11 and CASP12 came from more sophisticated statistical models and largely conventional machine learning.

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What it connects

Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII → states provision → CASP13 contact-prediction improvement attributed to deep learning

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Claim and evidence

Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIIIstates provision ↓CASP13 contact-prediction improvement attributed to deep learning

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Provision: Statement · provision ID mod-casp13-contact-precision
Date: 2019-10-23 · publication Records dated 2019 →

Scope and limitsThe CASP organisers' attribution across rounds, in their overview of CASP13, as stated in a figure discussion; the underlying measurements are not reproduced here.

The evidence 01

primary sourceSupports 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

Cite the original source

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Source excerpt

“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.”

Location in source discussion of contact-prediction progress across CASP rounds

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 (36 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.

Relation. An exact framework edition, or another document with an exact expression, states one provision: a capability threshold or risk level and its trigger, a required safeguard or action, a decision authority (who decides, recommends, can overrule or is informed), a stated exception, or another statement of its content, such as a licence term or a prospectus statement. The passage records what the document says, not implementation, compliance or effectiveness. Provisions with the same key align across editions of one family.

Date. Date given in PMC's "Published in final edited form" line for the organisers' overview paper (kind not stated).

Record ID aib-mod-casp13-contact-precision