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Interactive exploration of integrated biological datasets using context-sensitive workflows

Overview of attention for article published in Frontiers in Genetics, January 2014
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Title
Interactive exploration of integrated biological datasets using context-sensitive workflows
Published in
Frontiers in Genetics, January 2014
DOI 10.3389/fgene.2014.00021
Pubmed ID
Authors

Fabian Horn, Martin Rittweger, Jan Taubert, Artem Lysenko, Christopher Rawlings, Reinhard Guthke

Abstract

Network inference utilizes experimental high-throughput data for the reconstruction of molecular interaction networks where new relationships between the network entities can be predicted. Despite the increasing amount of experimental data, the parameters of each modeling technique cannot be optimized based on the experimental data alone, but needs to be qualitatively assessed if the components of the resulting network describe the experimental setting. Candidate list prioritization and validation builds upon data integration and data visualization. The application of tools supporting this procedure is limited to the exploration of smaller information networks because the display and interpretation of large amounts of information is challenging regarding the computational effort and the users' experience. The Ondex software framework was extended with customizable context-sensitive menus which allow additional integration and data analysis options for a selected set of candidates during interactive data exploration. We provide new functionalities for on-the-fly data integration using InterProScan, PubMed Central literature search, and sequence-based homology search. We applied the Ondex system to the integration of publicly available data for Aspergillus nidulans and analyzed transcriptome data. We demonstrate the advantages of our approach by proposing new hypotheses for the functional annotation of specific genes of differentially expressed fungal gene clusters. Our extension of the Ondex framework makes it possible to overcome the separation between data integration and interactive analysis. More specifically, computationally demanding calculations can be performed on selected sub-networks without losing any information from the whole network. Furthermore, our extensions allow for direct access to online biological databases which helps to keep the integrated information up-to-date.

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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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Israel 1 3%
Unknown 28 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 34%
Student > Ph. D. Student 7 24%
Student > Master 5 17%
Student > Bachelor 3 10%
Student > Doctoral Student 2 7%
Other 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 34%
Computer Science 6 21%
Biochemistry, Genetics and Molecular Biology 4 14%
Engineering 4 14%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 1 3%
Unknown 3 10%
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 05 March 2014.
All research outputs
#17,713,929
of 22,745,803 outputs
Outputs from Frontiers in Genetics
#6,041
of 11,758 outputs
Outputs of similar age
#220,782
of 305,223 outputs
Outputs of similar age from Frontiers in Genetics
#40
of 54 outputs
Altmetric has tracked 22,745,803 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 11,758 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 40th percentile – i.e., 40% 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 305,223 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.