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    Mining protein interactomes to improve their reliability and support the advancement of network medicine

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    fgene-06-00296.pdf
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    Type
    Article
    Authors
    Alanis Lobato, Gregorio cc
    KAUST Department
    Biological and Environmental Sciences and Engineering (BESE) Division
    Computer Science Program
    Integrative Systems Biology Lab
    Date
    2015-09-23
    Permanent link to this record
    http://hdl.handle.net/10754/581515
    
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    Abstract
    High-throughput detection of protein interactions has had a major impact in our understanding of the intricate molecular machinery underlying the living cell, and has permitted the construction of very large protein interactomes. The protein networks that are currently available are incomplete and a significant percentage of their interactions are false positives. Fortunately, the structural properties observed in good quality social or technological networks are also present in biological systems. This has encouraged the development of tools, to improve the reliability of protein networks and predict new interactions based merely on the topological characteristics of their components. Since diseases are rarely caused by the malfunction of a single protein, having a more complete and reliable interactome is crucial in order to identify groups of inter-related proteins involved in disease etiology. These system components can then be targeted with minimal collateral damage. In this article, an important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed. In addition to the review, a few representative examples of how molecular and clinical data can be integrated to deepen our understanding of pathogenesis are discussed.
    Citation
    Mining protein interactomes to improve their reliability and support the advancement of network medicine 2015, 6 Frontiers in Genetics
    Publisher
    Frontiers Media SA
    Journal
    Frontiers in Genetics
    DOI
    10.3389/fgene.2015.00296
    PubMed ID
    26442112
    Additional Links
    http://journal.frontiersin.org/Article/10.3389/fgene.2015.00296/abstract
    ae974a485f413a2113503eed53cd6c53
    10.3389/fgene.2015.00296
    Scopus Count
    Collections
    Articles; Biological and Environmental Science and Engineering (BESE) Division; Computer Science Program

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