Optimal Approximation and Model Quality Estimation for Nonlinear Systems

2003 ◽  
Vol 36 (16) ◽  
pp. 1867-1872 ◽  
Author(s):  
P.M. Mäkilä
Keyword(s):  
Nonlinear Systems ◽  
Optimal Approximation ◽  
Model Quality ◽  
Quality Estimation

scholarly journals Statistical Learning for Service Quality Estimation in Broadband PLC AMI

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 684 ◽  
Author(s):  
Dong Kim ◽  
Beom Chung ◽  
Young Chung
Keyword(s):  
Success Rate ◽  
Statistical Learning ◽  
Power Line Communication ◽  
Test Results ◽  
Model Quality ◽  
Quality Estimation ◽  
Rate Estimate ◽  
Communication Performance ◽  
Time Of Use Pricing ◽  
Advanced Metering

In this paper, we propose a method to estimate communication performance for the advanced metering infrastructure that employs the power line communication (PLC) technology. Using bit-per-symbol signals from the PLC network management system, we estimate a PLC model quality in terms of packet success rate based on statistical learning. We also verify the accuracy of the estimations by comparing them with measured communication test results at test sites. Finally, from the packet success rate estimate, the qualities of services, such as meter readings and time-of-use pricing data downloading under several metering protocol sequences, are investigated through a mathematical analysis, and numerical results are provided.

scholarly journals QMEANDisCo—distance constraints applied on model quality estimation

2019 ◽  
Vol 36 (6) ◽  
pp. 1765-1771 ◽  
Author(s):  
Gabriel Studer ◽  
Christine Rempfer ◽  
Andrew M Waterhouse ◽  
Rafal Gumienny ◽  
Juergen Haas ◽  
...  
Keyword(s):  
Protein Structures ◽  
Composite Score ◽  
Supplementary Information ◽  
Reference Structure ◽  
Distance Constraint ◽  
Consensus Methods ◽  
Single Model ◽  
Model Quality ◽  
Quality Estimation ◽  
Model Composite

Abstract Motivation Methods that estimate the quality of a 3D protein structure model in absence of an experimental reference structure are crucial to determine a model’s utility and potential applications. Single model methods assess individual models whereas consensus methods require an ensemble of models as input. In this work, we extend the single model composite score QMEAN that employs statistical potentials of mean force and agreement terms by introducing a consensus-based distance constraint (DisCo) score. Results DisCo exploits distance distributions from experimentally determined protein structures that are homologous to the model being assessed. Feed-forward neural networks are trained to adaptively weigh contributions by the multi-template DisCo score and classical single model QMEAN parameters. The result is the composite score QMEANDisCo, which combines the accuracy of consensus methods with the broad applicability of single model approaches. We also demonstrate that, despite being the de-facto standard for structure prediction benchmarking, CASP models are not the ideal data source to train predictive methods for model quality estimation. For performance assessment, QMEANDisCo is continuously benchmarked within the CAMEO project and participated in CASP13. For both, it ranks among the top performers and excels with low response times. Availability and implementation QMEANDisCo is available as web-server at https://swissmodel.expasy.org/qmean. The source code can be downloaded from https://git.scicore.unibas.ch/schwede/QMEAN. Supplementary information Supplementary data are available at Bioinformatics online.

Model quality in identification of nonlinear systems

2005 ◽  
Vol 50 (10) ◽  
pp. 1606-1611 ◽  
Author(s):  
M. Milanese ◽  
C. Novara
Keyword(s):  
Nonlinear Systems ◽  
Model Quality

scholarly journals QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i285-i291 ◽  
Author(s):  
Md Hossain Shuvo ◽  
Sutanu Bhattacharya ◽  
Debswapna Bhattacharya
Keyword(s):  
Neural Networks ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Residue Level ◽  
Supplementary Information ◽  
Model Quality ◽  
Quality Estimation ◽  
Distance Information ◽  
Protein Model

Abstract Motivation Protein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction. Results We present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently outperforms existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep. Availability and implementation https://github.com/Bhattacharya-Lab/QDeep. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks

2020 ◽  
Author(s):  
Md Hossain Shuvo ◽  
Sutanu Bhattacharya ◽  
Debswapna Bhattacharya
Keyword(s):  
Neural Networks ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Residue Level ◽  
Estimation Methods ◽  
Model Quality ◽  
Quality Estimation ◽  
Distance Information ◽  
Protein Model

AbstractMotivationProtein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction.ResultsWe present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently out-performs existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep.Availabilityhttps://github.com/Bhattacharya-Lab/[email protected]

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