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dc.contributor.authorTheofilatos, Konstantinos A.
dc.contributor.authorDimitrakopoulos, Christos M.
dc.contributor.authorLikothanassis, Spiridon D.
dc.contributor.authorKleftogiannis, Dimitrios A.
dc.contributor.authorMoschopoulos, Charalampos N.
dc.contributor.authorAlexakos, Christos
dc.contributor.authorPapadimitriou, Stergios
dc.contributor.authorMavroudi, Seferina P.
dc.date.accessioned2015-08-03T11:13:09Z
dc.date.available2015-08-03T11:13:09Z
dc.date.issued2013-07-12
dc.identifier.issn02692821
dc.identifier.doi10.1007/s10462-013-9409-8
dc.identifier.urihttp://hdl.handle.net/10754/562861
dc.description.abstractProteins 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.
dc.description.sponsorshipThis 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.
dc.publisherSpringer Nature
dc.subjectGenetic algorithms
dc.subjectHuman
dc.subjectKalman Filters
dc.subjectKnowledge base
dc.subjectPPI scoring methods
dc.subjectProtein–protein interactions
dc.titleThe human interactome knowledge base (hint-kb): An integrative human protein interaction database enriched with predicted protein–protein interaction scores using a novel hybrid technique
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalArtificial Intelligence Review
dc.contributor.institutionDepartment of Computer Engineering and Informatics, University of Patras, Building B, University Campus RioPatras, Greece
dc.contributor.institutionDepartment of Electrical Engineering-ESAT, SCD-SISTA, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Bus 2446Heverlee, Belgium
dc.contributor.institutioniMinds Future Health Department, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Bus 2446Heverlee, Belgium
dc.contributor.institutionDepartment of Computer Engineering and Informatics, Technological Institute of KavalaKavala, Greece
dc.contributor.institutionDepartment of Social Work, School of Sciences of Health and Care, Technological Educational Institute of PatrasPatras, Greece
kaust.personKleftogiannis, Dimitrios A.
dc.date.published-online2013-07-12
dc.date.published-print2014-10


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