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

Handle URI:
http://hdl.handle.net/10754/562861
Title:
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
Authors:
Theofilatos, Konstantinos A.; Dimitrakopoulos, Christos M.; Likothanassis, Spiridon D.; Kleftogiannis, Dimitrios A. ( 0000-0003-1086-821X ) ; Moschopoulos, Charalampos N.; Alexakos, Christos; Papadimitriou, Stergios; Mavroudi, Seferina P.
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Springer Nature
Journal:
Artificial Intelligence Review
Issue Date:
12-Jul-2013
DOI:
10.1007/s10462-013-9409-8
Type:
Article
ISSN:
02692821
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.
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorTheofilatos, Konstantinos A.en
dc.contributor.authorDimitrakopoulos, Christos M.en
dc.contributor.authorLikothanassis, Spiridon D.en
dc.contributor.authorKleftogiannis, Dimitrios A.en
dc.contributor.authorMoschopoulos, Charalampos N.en
dc.contributor.authorAlexakos, Christosen
dc.contributor.authorPapadimitriou, Stergiosen
dc.contributor.authorMavroudi, Seferina P.en
dc.date.accessioned2015-08-03T11:13:09Zen
dc.date.available2015-08-03T11:13:09Zen
dc.date.issued2013-07-12en
dc.identifier.issn02692821en
dc.identifier.doi10.1007/s10462-013-9409-8en
dc.identifier.urihttp://hdl.handle.net/10754/562861en
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.en
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.en
dc.publisherSpringer Natureen
dc.subjectGenetic algorithmsen
dc.subjectHumanen
dc.subjectKalman Filtersen
dc.subjectKnowledge baseen
dc.subjectPPI scoring methodsen
dc.subjectProtein–protein interactionsen
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 techniqueen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalArtificial Intelligence Reviewen
dc.contributor.institutionDepartment of Computer Engineering and Informatics, University of Patras, Building B, University Campus RioPatras, Greeceen
dc.contributor.institutionDepartment of Electrical Engineering-ESAT, SCD-SISTA, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Bus 2446Heverlee, Belgiumen
dc.contributor.institutioniMinds Future Health Department, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Bus 2446Heverlee, Belgiumen
dc.contributor.institutionDepartment of Computer Engineering and Informatics, Technological Institute of KavalaKavala, Greeceen
dc.contributor.institutionDepartment of Social Work, School of Sciences of Health and Care, Technological Educational Institute of PatrasPatras, Greeceen
kaust.authorKleftogiannis, Dimitrios A.en
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