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msBiodat analysis tool, big data analysis for high-throughput experiments

Overview of attention for article published in BioData Mining, August 2016
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Title
msBiodat analysis tool, big data analysis for high-throughput experiments
Published in
BioData Mining, August 2016
DOI 10.1186/s13040-016-0104-6
Pubmed ID
Authors

Pau M. Muñoz-Torres, Filip Rokć, Robert Belužic, Ivana Grbeša, Oliver Vugrek

Abstract

Mass spectrometry (MS) are a group of a high-throughput techniques used to increase knowledge about biomolecules. They produce a large amount of data which is presented as a list of hundreds or thousands of proteins. Filtering those data efficiently is the first step for extracting biologically relevant information. The filtering may increase interest by merging previous data with the data obtained from public databases, resulting in an accurate list of proteins which meet the predetermined conditions. In this article we present msBiodat Analysis Tool, a web-based application thought to approach proteomics to the big data analysis. With this tool, researchers can easily select the most relevant information from their MS experiments using an easy-to-use web interface. An interesting feature of msBiodat analysis tool is the possibility of selecting proteins by its annotation on Gene Ontology using its Gene Id, ensembl or UniProt codes. The msBiodat analysis tool is a web-based application that allows researchers with any programming experience to deal with efficient database querying advantages. Its versatility and user-friendly interface makes easy to perform fast and accurate data screening by using complex queries. Once the analysis is finished, the result is delivered by e-mail. msBiodat analysis tool is freely available at http://msbiodata.irb.hr.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Cuba 1 3%
Unknown 31 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 28%
Student > Ph. D. Student 4 13%
Professor > Associate Professor 4 13%
Student > Master 3 9%
Student > Doctoral Student 2 6%
Other 8 25%
Unknown 2 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 16%
Medicine and Dentistry 4 13%
Computer Science 4 13%
Biochemistry, Genetics and Molecular Biology 3 9%
Engineering 2 6%
Other 8 25%
Unknown 6 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 23 August 2016.
All research outputs
#17,812,737
of 22,883,326 outputs
Outputs from BioData Mining
#249
of 308 outputs
Outputs of similar age
#248,359
of 343,548 outputs
Outputs of similar age from BioData Mining
#8
of 8 outputs
Altmetric has tracked 22,883,326 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 308 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 16th percentile – i.e., 16% 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 343,548 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.