Visual data mining of biological networks: one size does not fit all

PLoS Comput Biol. 2013;9(1):e1002833. doi: 10.1371/journal.pcbi.1002833. Epub 2013 Jan 10.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aging / genetics
  • Computational Biology*
  • Humans
  • Information Storage and Retrieval*
  • Neoplasms / genetics
  • Neoplasms / pathology
  • Vision, Ocular*

Grants and funding

This research was funded in part by Ontario Research Fund (GL2-01-030), Ontario Research Fund (GL2-01-030), Canada Foundation for Innovation (CFI #12301, CFI #203373 and CFI #29272), and the Ontario Ministry of Health and Long Term Care. The views expressed do not necessarily reflect those of the OMOHLTC. CP was funded in part by Friuli Exchange Program. IJ is supported in part by the Canada Research Chair Program (CRC #203373 and #225404). The funders had no role in the preparation of the manuscript.