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

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 u...

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Detalles Bibliográficos
Autores principales: Pastrello, Chiara, Otasek, David, Fortney, Kristen, Agapito, Giuseppe, Cannataro, Mario, Shirdel, Elize, Jurisica, Igor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3547662/
https://www.ncbi.nlm.nih.gov/pubmed/23341759
http://dx.doi.org/10.1371/journal.pcbi.1002833
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author Pastrello, Chiara
Otasek, David
Fortney, Kristen
Agapito, Giuseppe
Cannataro, Mario
Shirdel, Elize
Jurisica, Igor
author_facet Pastrello, Chiara
Otasek, David
Fortney, Kristen
Agapito, Giuseppe
Cannataro, Mario
Shirdel, Elize
Jurisica, Igor
author_sort Pastrello, Chiara
collection PubMed
description 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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spelling pubmed-35476622013-01-22 Visual Data Mining of Biological Networks: One Size Does Not Fit All Pastrello, Chiara Otasek, David Fortney, Kristen Agapito, Giuseppe Cannataro, Mario Shirdel, Elize Jurisica, Igor PLoS Comput Biol Education 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. Public Library of Science 2013-01-10 /pmc/articles/PMC3547662/ /pubmed/23341759 http://dx.doi.org/10.1371/journal.pcbi.1002833 Text en © 2013 Pastrello et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Education
Pastrello, Chiara
Otasek, David
Fortney, Kristen
Agapito, Giuseppe
Cannataro, Mario
Shirdel, Elize
Jurisica, Igor
Visual Data Mining of Biological Networks: One Size Does Not Fit All
title Visual Data Mining of Biological Networks: One Size Does Not Fit All
title_full Visual Data Mining of Biological Networks: One Size Does Not Fit All
title_fullStr Visual Data Mining of Biological Networks: One Size Does Not Fit All
title_full_unstemmed Visual Data Mining of Biological Networks: One Size Does Not Fit All
title_short Visual Data Mining of Biological Networks: One Size Does Not Fit All
title_sort visual data mining of biological networks: one size does not fit all
topic Education
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3547662/
https://www.ncbi.nlm.nih.gov/pubmed/23341759
http://dx.doi.org/10.1371/journal.pcbi.1002833
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