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IMEx Databases: Displaying Molecular Interactions into a Single, Standards-Compliant Dataset

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Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2449))

Abstract

Molecular interaction databases aim to systematically capture and organize the experimental interaction information described in the scientific literature. These data can then be used to perform network analysis, to assign putative roles to uncharacterized proteins and to investigate their involvement in cellular pathways.

This chapter gives a brief overview of publicly available molecular interaction databases and focuses on the members of the IMEx Consortium, on their curation policies and standard data formats. All of the goals achieved by IMEx databases over the last 15 years, the data types provided and the many different ways in which such data can be utilized by the research community, are described in detail. The IMEx databases curate molecular interaction data to the highest caliber, following a detailed curation model and supplying rich metadata by employing common curation rules and harmonized standards. The IMEx Consortium provides comprehensively annotated molecular interaction data integrated into a single, non-redundant, open access dataset.

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Author information

Authors and Affiliations

  1. European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK

    Pablo Porras & Sandra Orchard

  2. Department of Biology, University of Rome Tor Vergata, Rome, Italy

    Luana Licata

Authors
  1. Pablo Porras
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  2. Sandra Orchard
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  3. Luana Licata
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Corresponding author

Correspondence to Luana Licata .

Editor information

Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Oliviero Carugo

  2. Genome Institute and Bioinformatics Institute, Singapore, Singapore

    Frank Eisenhaber

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Porras, P., Orchard, S., Licata, L. (2022). IMEx Databases: Displaying Molecular Interactions into a Single, Standards-Compliant Dataset. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 2449. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2095-3_2

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  • DOI: https://doi.org/10.1007/978-1-0716-2095-3_2

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2094-6

  • Online ISBN: 978-1-0716-2095-3

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