ArticleProteins2016
CASP11 statistics and the prediction center evaluation system.
Article in Proteins, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 43 citations in OpenAlex.
- Assessment of the Performances of the Protein Modeling Techniques Participating in CASP15 Using a Structure-Based Functional Site Prediction Approach: ResiRole.Bioengineering (Basel, Switzerland) · 2023Article
- Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes.Annual review of biophysics · 2023Review
- Beyond sequence: Structure-based machine learning.Computational and structural biotechnology journal · 2023Review
- Target classification in the 14th round of the critical assessment of protein structure prediction (CASP14).Proteins · 2021Article
- Article
- Modeling SARS-CoV-2 proteins in the CASP-commons experiment.Proteins · 2021Article
- Assessment of domain interactions in the fourteenth round of the Critical Assessment of Structure Prediction (CASP14).Proteins · 2021Article
- SAXSDom: Modeling multidomain protein structures using small-angle X-ray scattering data.Proteins · 2020Article
- Article
- Evaluation of model refinement in CASP13.Proteins · 2019Article
- Evaluation of template-based modeling in CASP13.Proteins · 2019Article
- Article
- Methods for the Refinement of Protein Structure 3D Models.International journal of molecular sciences · 2019Review
- End-to-End Differentiable Learning of Protein Structure.Cell systems · 2019Article
- Evaluation system and web infrastructure for the second cryo-EM model challenge.Journal of structural biology · 2018Article
- Article
- Assessment of model accuracy estimations in CASP12.Proteins · 2018Article
- Evaluation of the template-based modeling in CASP12.Proteins · 2018Article
- Assessment of the model refinement category in CASP12.Proteins · 2018Article
- Assessment of protein assembly prediction in CASP12.Proteins · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
We outline the role of the Protein Structure Prediction Center (predictioncenter.org) in conducting the CASP11 and CASP ROLL experiments, discuss the experiment statistics, and provide an overview of the present CASP infrastructure. The biggest changes compared to the previous CASPs are the implementation of the evaluation system incorporating practically all evaluation measures, statistical tests, and visualization tools historically used by the CASP assessors, the expansion of the infrastructure to incorporate new categories of contact-assisted and multimeric predictions, and the redesign of the assessors' web-workspace enabling assessments based on multiple measures for different group categories and target sets. Proteins 2016; 84(Suppl 1):15-19. © 2016 Wiley Periodicals, Inc.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.