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
Type
ArticleAuthors
Theofilatos, Konstantinos A.Dimitrakopoulos, Christos M.
Likothanassis, Spiridon D.
Kleftogiannis, Dimitrios A.
Moschopoulos, Charalampos N.
Alexakos, Christos
Papadimitriou, Stergios
Mavroudi, Seferina P.
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Date
2013-07-12Online Publication Date
2013-07-12Print Publication Date
2014-10Permanent link to this record
http://hdl.handle.net/10754/562861Metadata
Show full item recordAbstract
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/s10462-013-9409-8Sponsors
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 NatureJournal
Artificial Intelligence ReviewScopus Count
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