ArticleBMC bioinformatics2016
DeepQA: improving the estimation of single protein model quality with deep belief networks.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 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
59 citing papers in PubMed.
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment.Briefings in bioinformatics · 2025Article
- Recent advances and challenges in protein complex model accuracy estimation.Computational and structural biotechnology journal · 2024Review
- Identification of FDA-approved drugs with triple targeting mode of action for the treatment of monkeypox: a high throughput virtual screening study.Molecular diversity · 2024Article
- Protein structure accuracy estimation using geometry-complete perceptron networks.Protein science : a publication of the Protein Society · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- 3D-equivariant graph neural networks for protein model quality assessment.Bioinformatics (Oxford, England) · 2023Article
- Predicting residue-specific qualities of individual protein models using residual neural networks and graph neural networks.Proteins · 2022Article
- Article
- DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network.BMC bioinformatics · 2022Article
- EnsembleFam: towards more accurate protein family prediction in the twilight zone.BMC bioinformatics · 2022Article
- ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features.Briefings in bioinformatics · 2022Article
- An integrated protein structure fitness scoring approach for identifying native-like model structures.Computational and structural biotechnology journal · 2022Article
- Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14.Proteins · 2022Article
- Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14.Scientific reports · 2021Article
- MUfoldQA_G: High-accuracy protein model QA via retraining and transformation.Computational and structural biotechnology journal · 2021Article
- Decoy selection for protein structure prediction via extreme gradient boosting and ranking.BMC bioinformatics · 2020Article
- Empirical Comparison and Analysis of Web-Based DNAMolecular therapy. Nucleic acids · 2020Article
- Using physical features of protein core packing to distinguish real proteins from decoys.Protein science : a publication of the Protein Society · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundProtein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem.
resultsWe introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods.
conclusionDeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .
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.