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Ki MoSys: a web-based repository of experimental data for KInetic MOdels of biological SYStems

Overview of attention for article published in BMC Systems Biology, August 2014
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Title
Ki MoSys: a web-based repository of experimental data for KInetic MOdels of biological SYStems
Published in
BMC Systems Biology, August 2014
DOI 10.1186/s12918-014-0085-3
Pubmed ID
Authors

Rafael S Costa, André Veríssimo, Susana Vinga

Abstract

BackgroundThe kinetic modeling of biological systems is mainly composed of three steps that proceed iteratively: model building, simulation and analysis. In the first step, it is usually required to set initial metabolite concentrations, and to assign kinetic rate laws, along with estimating parameter values using kinetic data through optimization when these are not known. Although the rapid development of high-throughput methods has generated much omics data, experimentalists present only a summary of obtained results for publication, the experimental data files are not usually submitted to any public repository, or simply not available at all. In order to automatize as much as possible the steps of building kinetic models, there is a growing requirement in the systems biology community for easily exchanging data in combination with models, which represents the main motivation of KiMoSys development.Description KiMoSys is a user-friendly platform that includes a public data repository of published experimental data, containing concentration data of metabolites and enzymes and flux data. It was designed to ensure data management, storage and sharing for a wider systems biology community. This community repository offers a web-based interface and upload facility to turn available data into publicly accessible, centralized and structured-format data files. Moreover, it compiles and integrates available kinetic models associated with the data. KiMoSys also integrates some tools to facilitate the kinetic model construction process of large-scale metabolic networks, especially when the systems biologists perform computational research.Conclusions KiMoSys is a web-based system that integrates a public data and associated model(s) repository with computational tools, providing the systems biology community with a novel application facilitating data storage and sharing, thus supporting construction of ODE-based kinetic models and collaborative research projects.The web application implemented using Ruby on Rails framework is freely available for web access at http://kimosys.org, along with its full documentation.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 2%
United States 1 2%
Russia 1 2%
Singapore 1 2%
Unknown 54 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 26%
Student > Ph. D. Student 11 19%
Student > Bachelor 7 12%
Student > Master 7 12%
Professor > Associate Professor 5 9%
Other 8 14%
Unknown 5 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 24%
Engineering 9 16%
Computer Science 9 16%
Biochemistry, Genetics and Molecular Biology 6 10%
Medicine and Dentistry 3 5%
Other 8 14%
Unknown 9 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 August 2014.
All research outputs
#14,198,795
of 22,760,687 outputs
Outputs from BMC Systems Biology
#544
of 1,142 outputs
Outputs of similar age
#119,176
of 231,138 outputs
Outputs of similar age from BMC Systems Biology
#12
of 25 outputs
Altmetric has tracked 22,760,687 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 231,138 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.