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    The human interactome knowledge base (hint-kb): An integrative human protein interaction database enriched with predicted protein–protein interaction scores using a novel hybrid technique

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    Type
    Article
    Authors
    Theofilatos, Konstantinos A.
    Dimitrakopoulos, Christos M.
    Likothanassis, Spiridon D.
    Kleftogiannis, Dimitrios A. cc
    Moschopoulos, Charalampos N.
    Alexakos, Christos
    Papadimitriou, Stergios
    Mavroudi, Seferina P.
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2013-07-12
    Online Publication Date
    2013-07-12
    Print Publication Date
    2014-10
    Permanent link to this record
    http://hdl.handle.net/10754/562861
    
    Metadata
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    Abstract
    Proteins are the functional components of many cellular processes and the identification of their physical protein–protein interactions (PPIs) is an area of mature academic research. Various databases have been developed containing information about experimentally and computationally detected human PPIs as well as their corresponding annotation data. However, these databases contain many false positive interactions, are partial and only a few of them incorporate data from various sources. To overcome these limitations, we have developed HINT-KB (http://biotools.ceid.upatras.gr/hint-kb/), a knowledge base that integrates data from various sources, provides a user-friendly interface for their retrieval, cal-culatesasetoffeaturesofinterest and computesaconfidence score for every candidate protein interaction. This confidence score is essential for filtering the false positive interactions which are present in existing databases, predicting new protein interactions and measuring the frequency of each true protein interaction. For this reason, a novel machine learning hybrid methodology, called (Evolutionary Kalman Mathematical Modelling—EvoKalMaModel), was used to achieve an accurate and interpretable scoring methodology. The experimental results indicated that the proposed scoring scheme outperforms existing computational methods for the prediction of PPIs.
    Citation
    Theofilatos, K., Dimitrakopoulos, C., Likothanassis, S., Kleftogiannis, D., Moschopoulos, C., Alexakos, C., … Mavroudi, S. (2013). The Human Interactome Knowledge Base (HINT-KB): an integrative human protein interaction database enriched with predicted protein–protein interaction scores using a novel hybrid technique. Artificial Intelligence Review, 42(3), 427–443. doi:10.1007/s10462-013-9409-8
    Sponsors
    This research has been co-financed by the European Union (European Social Fund-ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF)-Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund.
    Publisher
    Springer Nature
    Journal
    Artificial Intelligence Review
    DOI
    10.1007/s10462-013-9409-8
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10462-013-9409-8
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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