ModelEvaluator statistics

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ModelEvaluator specifications


Unique identifier OMICS_11953
Name ModelEvaluator
Software type Package/Module
Interface Command line interface
Restrictions to use Academic or non-commercial use
Operating system Unix/Linux
Computer skills Advanced
Version 1.0
Stability Stable
Maintained Yes



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Publication for ModelEvaluator

ModelEvaluator in publications

PMCID: 4619059
PMID: 26493701
DOI: 10.1186/s12859-015-0775-x

[…] above., the pool of predicted models was evaluated by 14 quality assessment (qa) methods (e.g., multicom-novel_qa – a new in-house single qa method, modfoldclust2 [], proq2 [], pcons [], apollo [], modelevaluator [], modelcheck2 – an improved version of modelevaluator, qapro – a weighted combination of modelevaluator and apollo, selectpro [], dope [], dfire2 [], opus_psp [], rwplus [], […]

PMCID: 4553833
PMID: 26072473
DOI: 10.1093/bioinformatics/btv235

[…] the protein sequence and those of a model parsed by dssp (), physical-chemical features (i.e. surface polar score, weighted exposed score, and etc.) (), the normalized quality score generated by modelevaluator (), rwplus score (), dope score () and rf_cb_srs_od score (); proq2 (); model check2 method produced by an improved version of modelevaluator (); a recalibrated selectpro energy (); […]

PMCID: 4705550
PMID: 26752865
DOI: 10.4172/jpb.S9-001

[…] []. accurate quality assessment of protein models can help rank a pool of candidate models predicted for a given query protein. a number of model quality assessment methods and tools, such as modelevaluator [], apollo [], qmean [], have been developed. these methods evaluate the quality of models based on the structural information extracted from protein models, without considering […]

PMCID: 3200154
PMID: 21989082
DOI: 10.1186/1472-6807-11-38

[…] poor quality models and verify that the method was not relying on a small number of good models to make quality predictions. three filtering processes were applied. in the first approach, we used modelevaluator [] to predict the quality of each model and then removed those models from the ensemble whose predicted quality was below a set threshold. more specifically, we used the predicted […]

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ModelEvaluator institution(s)
Computer Science Department, Informatics Institute, University of Missouri, Columbia, MO, USA
ModelEvaluator funding source(s)
MU Faculty startup Grant, MU Research Board Grant, MU Bioinformatics Consortium.

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