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Provar specifications

Information


Unique identifier OMICS_10822
Name Provar
Alternative name Probability of variation
Software type Package/Module
Interface Command line interface
Restrictions to use None
Output data The outputs are probabilities that each atom or residue of the protein borders a predicted pocket. These probabilities can be readily visualised on a protein using existing molecular graphics software.
Operating system Unix/Linux, Mac OS, Windows
Programming languages MATLAB
Computer skills Advanced
Stability Stable
Requirements
Statistics toolbox
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline Paul Ashford

Publication for Probability of variation

Provar citations

 (18)
library_books

Phenotypic Trait Identification Using a Multimodel Bayesian Method: A Case Study Using Photosynthesis in Brassica rapa Genotypes

2018
Front Plant Sci
PMCID: 5913710
PMID: 29719545
DOI: 10.3389/fpls.2018.00448

[…] ng posterior boxplots and an analysis of HDI's. Trait distributions showed some parameters with high probability of genotypic variation, including Jmax, Vcmax, Γ*, and EVcmax, and traits with limited probability of variation, including Ko, ϕJ and θJ (Figures –). Vcmax, Γ*, Rd, Kc, and Ko were the five traits estimated in all eight models. Vcmax showed genotypic variance in all models with r46 nota […]

library_books

Comparison Between Different Strategies of Rheumatic Heart Disease Echocardiographic Screening in Brazil: Data From the PROVAR (Rheumatic Valve Disease Screening Program) Study

2018
PMCID: 5850205
PMID: 29444774
DOI: 10.1161/JAHA.117.008039

[…] iological features of streptococcal infections may be more complex than anticipated in these areas, and other factors besides socioeconomic status should be further evaluated.Strategies tested in the PROVAR (Rheumatic Valve Disease Screening Program) study, such as task shifting, computer‐based training, use of portable and handheld affordable devices, telemedicine for education purposes, and remo […]

library_books

Forecasting the Amount of Waste Sewage Water Discharged into the Yangtze River Basin Based on the Optimal Fractional Order Grey Model

2017
PMCID: 5800120
PMID: 29295517
DOI: 10.3390/ijerph15010020

[…] o gBest, the new position is set to gBest.Step 5: Computing the variance σ2 of the group fitness and f(gBest). (22)σ2=∑i=1n(fi−favgf)2 (23)f={max{|fi−favg|},{|fi−favg|}>11,othersStep 6: Computing the probability of variation pm. (24)pm={k,σ2<σd2 and f(gBest)>fd 0,othersStep 7: Generating random numbers ε∈[0,1], if ε

library_books

Proteomics analysis to reveal biological pathways and predictive proteins in the survival of high grade serous ovarian cancer

2017
Sci Rep
PMCID: 5575023
PMID: 28852147
DOI: 10.1038/s41598-017-10559-9

[…] in markers significantly associated with progression free survival (PFS) based on the least absolute shrinkage and selection operator (lasso) and constructed a protein-driven index of ovarian cancer (PROVAR) scores to predict the recurrence time for ovarian cancer patients. However, Zhang et al. performed an external validation in 67 patients and found that the PROVAR signature was prognosis of su […]

library_books

Allosteric Communication Networks in Proteins Revealed through Pocket Crosstalk Analysis

2017
PMCID: 5620967
PMID: 28979936
DOI: 10.1021/acscentsci.7b00211

[…] MDpocket, PocketAnalizerPCA, Epock, Trj_cavity, and TRAPP). In this case, the resulting information may depend on the specific reference structure used for the alignment. Atom-based algorithms (e.g., PROVAR and EPOSBP) avoid the alignment step. Nevertheless, most of these methods are limited to analyzing a priori defined pocket(s) of interest only. Another key aspect is that pockets can sometime t […]

library_books

Adaptive temperature regulation in the little bird in winter: predictions from a stochastic dynamic programming model

2017
PMCID: 5596050
PMID: 28776203
DOI: 10.1007/s00442-017-3923-3

[…] good weather t < T f, the dynamic programming equation becomes:5FG(x,y,t)=maxi(1-βi)pGGλiFG(x′,y′,t+1)+(1-λi)FG(x″,y′,t+1)+(1-pGG)λiFB(x″′,y′,t+1)+(1-λi)FB(x″″,y′,t+1),where i is behaviour, λ is the probability of variation when performing this behaviour (for example, the amount of food found when foraging), and β is the instantaneous predation risk. As there are separate functions for good (F G) […]

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Provar institution(s)
Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London, Malet Street, London, UK; Pfizer Global Research and Development, Ramsgate Road, Sandwich, UK

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