gRINN statistics

To access cutting-edge analytics on consensus tools, life science contexts and associated fields, you will need to subscribe to our premium service.

Subscribe
info

Citations per year

Citations chart
info

Popular tool citations

chevron_left Dynamics prediction chevron_right
Popular tools chart
info

Tool usage distribution map

Tool usage distribution map
info

Associated diseases

Associated diseases

gRINN specifications

Information


Unique identifier OMICS_29671
Name gRINN
Alternative name get Residue Interaction eNergies and Networks
Software type Application/Script
Interface Command line interface
Restrictions to use None
Output format CSV,TSV
Operating system Unix/Linux
Programming languages Python
Computer skills Advanced
Version 1.1.0
Stability Stable
Maintained Yes

Download


Versioning


Add your version

Maintainers


Additional information


https://bitbucket.org/onursercinoglu/grinn-docs/overview https://bitbucket.org/onursercinoglu/grinn-docs/src/f4604412c9ab09eceaed32e9f82fbc45e9b646ce/tutorial.html

Publication for get Residue Interaction eNergies and Networks

gRINN in publications

 (17)
PMCID: 5930333
PMID: 29475862
DOI: 10.1128/AEM.02601-17

[…] humidity is essential for growth of most mesophilic fungi and was considered a good predictor of fungal prevalence. the total variance explained by the rda model (ca. 22%) was higher than that of grinn-gofroń and bosiacka () (ca. 16.5%) and suggested that mean air temperature was the most important factor affecting the composition of airborne fungal spores. overall, our results suggest […]

PMCID: 5624939
PMID: 28970503
DOI: 10.1038/s41598-017-12624-9

[…] on metabolomics workbench and massive/proteome exchange, respectively. see supplementary information for accession numbers., an integrated network of metabolites and proteins was computed by grinn software tool, an r-based tool that integrates biochemical and genomic relationships from several databases, including kegg, reactome and ensembl. we used significant metabolites and proteins […]

PMCID: 5585187
PMID: 28919848
DOI: 10.3389/fnins.2017.00465

[…] with respect to type, level, and duration., noise levels were estimated using the smartphone app “spl graph,” installed on each participant's phone prior to event attendance. data presented by grinn et al. () showed this app to be accurate within 2-db of a class 1 sound level meter (slm) across 25 used (not-new) iphones (models 5, 5s, 6, 6s, 6s plus, and 7) for test signals including […]

PMCID: 5818593
PMID: 29497241
DOI: 10.1007/s10453-017-9493-3

[…] in weather conditions, including complex of variables. such complex interactions have been analyzed in a few studies, which considered only daily values of variables (hjelmroos ; li and kendrick , ; grinn-gofroń and bosiacka ; sadyś et al. )., the aim of this study was to analyze and compare the impact of meteorological parameters on daily and hourly concentrations and compositions of fungal […]

PMCID: 5473988
PMID: 28638442
DOI: 10.1186/s13040-017-0140-x

[…] correlation analysis with other types of relationships such as biochemical reactions and molecular structural and mass spectral similarity (metamapr)., in addition, they provide a dynamic interface (grinn) to integrate gene, protein, and metabolite data using more advanced biological-network-based approaches such as gaussian graphical models, partial correlation and bayesian networks for omics […]


To access a full list of publications, you will need to upgrade to our premium service.

gRINN institution(s)
Department of Bioengineering, Faculty of Engineering, Marmara University, Kadikoy, Istanbul, Turkey
gRINN funding source(s)
Supported by the OYP research grant from the Higher Education Council of Turkey and by Marmara University Commission of Scientific Research Project, and the FEN-A-100616-0273.

gRINN reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review gRINN