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Visual Data Mining of Biological Networks: One Size Does Not Fit All

Overview of attention for article published in PLoS Computational Biology, January 2013
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Title
Visual Data Mining of Biological Networks: One Size Does Not Fit All
Published in
PLoS Computational Biology, January 2013
DOI 10.1371/journal.pcbi.1002833
Pubmed ID
Authors

Chiara Pastrello, David Otasek, Kristen Fortney, Giuseppe Agapito, Mario Cannataro, Elize Shirdel, Igor Jurisica

Abstract

High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.

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X Demographics

The data shown below were collected from the profiles of 23 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 162 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 8%
Brazil 2 1%
Colombia 1 <1%
Germany 1 <1%
Netherlands 1 <1%
France 1 <1%
Italy 1 <1%
Hungary 1 <1%
Sweden 1 <1%
Other 7 4%
Unknown 133 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 49 30%
Student > Ph. D. Student 39 24%
Student > Master 15 9%
Student > Bachelor 12 7%
Professor > Associate Professor 12 7%
Other 25 15%
Unknown 10 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 85 52%
Computer Science 23 14%
Biochemistry, Genetics and Molecular Biology 18 11%
Medicine and Dentistry 6 4%
Engineering 4 2%
Other 15 9%
Unknown 11 7%