Assessment of predictions in the model quality assessment category
By Domenico Cozzetto, Andriy Kryshtafovych, Michele Ceriani, Anna Tramontano. 2007
129 citations recorded by the source corpus.
Subfields: Materials Chemistry, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Materials Chemistry
- Torsten Schwede
- Andriy Kryshtafovych
- Jianlin Cheng
- Liam J. McGuffin
- Björn Wallner
- Yang Zhang
- Krzysztof Fidelis
- John Moult
In Molecular Biology
- Torsten Schwede
- Jianlin Cheng
- Yang Zhang
- Andriy Kryshtafovych
- Liam J. McGuffin
- Björn Wallner
- Krzysztof Fidelis
- John Moult
Citations in the corpus
- QMEAN server for protein model quality estimation (2009, background): “As highlighted recently during the CASP blind-test experiment (14,15), correctly ranking different models for the same target protein is not a trivial task and structure prediction groups have considerable problems in ranking their own models.”
- SELECTpro: effective protein model selection using a structure-based energy function resistant to BLUNDERs (2008, methodology): “Pearson Correlation for Individual Proteins The assessor evaluation of the quality assessment category [18] focused on the Pearson Correlation between the QA scores and GDT-TS.”
- Improved model quality assessment using ProQ2 (2012, result): “Combining ProQ2 with Pcons It has been shown many times, both in CASP [25,28,29] and elsewhere [1,2,21], that consensus methods are superior MQAPs compared to stand-alone or single methods not using consensus or template information, at least in terms of correlation.”
- Machine Learning Methods for Protein Structure Prediction (2008, background): “For model quality evaluation, the correlation between predicted scores and real quality scores for hard targets (poor models) is still low [97], i.”
- Comparative modeling in structural genomics. (2008, background): “It transpired that the best methods are already usefully effective at assigning detailed accuracy (Cozzetto et al., 2007), although there is certainly a great deal of room for improvement.”
- GraphQA: protein model quality assessment using graph convolutional networks (2020, unknown): “Protein quality assessment methods are evaluated in CASP (Moult et al. , 1995) since CASP7 (Cozzetto et al. , 2007).”
- The ModFOLD server for the quality assessment of protein structural models (2008, methodology): “Figure 1 shows an example of the ModFOLDclust results for a model of CASP7 target T0284 compared with the observed error according to a superposition with the native structure.”