Title |
Visual Data Mining of Biological Networks: One Size Does Not Fit All
|
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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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 22% |
Germany | 3 | 13% |
Canada | 1 | 4% |
Singapore | 1 | 4% |
Japan | 1 | 4% |
Brazil | 1 | 4% |
United Arab Emirates | 1 | 4% |
Spain | 1 | 4% |
United Kingdom | 1 | 4% |
Other | 1 | 4% |
Unknown | 7 | 30% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 12 | 52% |
Members of the public | 10 | 43% |
Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
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% |