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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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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, calculates a set of features of interest and computes a confidence 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.

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Acknowledgments

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.

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Authors and Affiliations

  1. Department of Computer Engineering and Informatics, University of Patras, Building B, University Campus Rio, Patras, Greece

    Konstantinos Theofilatos, Christos Dimitrakopoulos, Spiros Likothanassis, Christos Alexakos & Seferina Mavroudi

  2. King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal, 23955-6900, Saudi Arabia

    Dimitrios Kleftogiannis

  3. Department of Electrical Engineering-ESAT, SCD-SISTA, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Bus 2446, 3001 , Heverlee, Belgium

    Charalampos Moschopoulos

  4. iMinds Future Health Department, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Bus 2446, 3001 , Heverlee, Belgium

    Charalampos Moschopoulos

  5. Department of Computer Engineering and Informatics, Technological Institute of Kavala, Kavala, Greece

    Stergios Papadimitriou

  6. Department of Social Work, School of Sciences of Health and Care, Technological Educational Institute of Patras, Patras, Greece

    Seferina Mavroudi

Authors
  1. Konstantinos Theofilatos
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  2. Christos Dimitrakopoulos
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  3. Spiros Likothanassis
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  4. Dimitrios Kleftogiannis
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  5. Charalampos Moschopoulos
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  6. Christos Alexakos
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  7. Stergios Papadimitriou
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  8. Seferina Mavroudi
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Correspondence to Konstantinos Theofilatos or Seferina Mavroudi.

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Theofilatos, K., Dimitrakopoulos, C., Likothanassis, S. et al. 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. Artif Intell Rev 42, 427–443 (2014). https://doi.org/10.1007/s10462-013-9409-8

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  • DOI: https://doi.org/10.1007/s10462-013-9409-8

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