ArticleScientific reports2016
Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Protein Structural Model Selection Informed by Comparison of Predicted Ligand Binding Poses.Journal of chemical information and modeling · 2025Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Predicting residue-specific qualities of individual protein models using residual neural networks and graph neural networks.Proteins · 2022Article
- MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials.BMC bioinformatics · 2020Review
- Machine Learning Approaches for Quality Assessment of Protein Structures.Biomolecules · 2020Review
- Predicting protein residue-residue contacts using random forests and deep networks.BMC bioinformatics · 2019Article
- Graph-Based Community Detection for Decoy Selection in Template-Free Protein Structure Prediction.Molecules (Basel, Switzerland) · 2019Article
- SOV_refine: A further refined definition of segment overlap score and its significance for protein structure similarity.Source code for biology and medicine · 2018Article
- An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences.Acta crystallographica. Section D, Structural biology · 2017Review
- QAcon: single model quality assessment using protein structural and contact information with machine learning techniques.Bioinformatics (Oxford, England) · 2017Article
- Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models.Scientific reports · 2017Article
- A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine.PloS one · 2017Article
- DeepQA: improving the estimation of single protein model quality with deep belief networks.BMC bioinformatics · 2016Article
- ProQ3: Improved model quality assessments using Rosetta energy terms.Scientific reports · 2016Article
- Sorting protein decoys by machine-learning-to-rank.Scientific reports · 2016Article
- Computational protein structure refinement: Almost there, yet still so far to go.Wiley interdisciplinary reviews. Computational molecular scienceArticle
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4 authors.
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Abstract
Quality assessment of a protein model is to predict the absolute or relative quality of a protein model using computational methods before the native structure is available. Single-model methods only need one model as input and can predict the absolute residue-specific quality of an individual model. Here, we have developed four novel single-model methods (Wang_deep_1, Wang_deep_2, Wang_deep_3, and Wang_SVM) based on stacked denoising autoencoders (SdAs) and support vector machines (SVMs). We evaluated these four methods along with six other methods participating in CASP11 at the global and local levels using Pearson's correlation coefficients and ROC analysis. As for residue-specific quality assessment, our four methods achieved better performance than most of the six other CASP11 methods in distinguishing the reliably modeled residues from the unreliable measured by ROC analysis; and our SdA-based method Wang_deep_1 has achieved the highest accuracy, 0.77, compared to SVM-based methods and our ensemble of an SVM and SdAs. However, we found that Wang_deep_2 and Wang_deep_3, both based on an ensemble of multiple SdAs and an SVM, performed slightly better than Wang_deep_1 in terms of ROC analysis, indicating that integrating an SVM with deep networks works well in terms of certain measurements.
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