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

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          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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          Most cited references26

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          Network-based classification of breast cancer metastasis

          Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
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            Network-based prediction of protein function

            Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.
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              IntAct—open source resource for molecular interaction data

              IntAct is an open source database and software suite for modeling, storing and analyzing molecular interaction data. The data available in the database originates entirely from published literature and is manually annotated by expert biologists to a high level of detail, including experimental methods, conditions and interacting domains. The database features over 126 000 binary interactions extracted from over 2100 scientific publications and makes extensive use of controlled vocabularies. The web site provides tools allowing users to search, visualize and download data from the repository. IntAct supports and encourages local installations as well as direct data submission and curation collaborations. IntAct source code and data are freely available from .
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                January 2013
                January 2013
                10 January 2013
                : 9
                : 1
                : e1002833
                Affiliations
                [1 ]CRO Aviano, National Cancer Institute, Aviano, Italy
                [2 ]Ontario Cancer Institute, the Campbell Family Institute for Cancer Research, and Techna Institute, University Health Network, Toronto, Ontario, Canada
                [3 ]Department of Medical and Surgical Sciences, University Magna Græcia, Catanzaro, Italy
                [4 ]Departments of Computer Science and Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
                Whitehead Institute, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Article
                PCOMPBIOL-D-12-01330
                10.1371/journal.pcbi.1002833
                3547662
                23341759
                e84f7b31-5782-4914-b3b5-d3b061d5db4e
                Copyright @ 2013

                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 credited.

                History
                Page count
                Pages: 6
                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.
                Categories
                Education
                Biology
                Computational Biology
                Signaling Networks
                Proteomics
                Protein Interactions

                Quantitative & Systems biology
                Quantitative & Systems biology

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