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Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study.

Villaveces JM, Jiménez RC, Porras P, Del-Toro N, Duesbury M, Dumousseau M, Orchard S, Choi H, Ping P, Zong NC, Askenazi M, Habermann BH, Hermjakob H - Database (Oxford) (2015)

Bottom Line: The evidence that two molecules interact in a living cell is often inferred from multiple different experiments.We have developed both a merging algorithm and a scoring system for molecular interactions based on the proteomics standard initiative-molecular interaction standards.In this manuscript, we introduce these two algorithms and provide community access to the tool suite, describe examples of how these tools are useful to selectively present molecular interaction data and demonstrate a case where the algorithms were successfully used to identify a systematic error in an existing dataset.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Matinsried, Germany, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Department of Physiology and Department of Medicine, Division of Cardiology, David Geffen School of Medicine at UCLA, 675 Charles E. Young Drive, MRL Building, Suite 1609, Los Angeles, California 90095, USA and Biomedical Hosting LLC, Arlington, Massachusetts 02474, USA.

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The MIscore normalized score calculates a composite score for an interaction based on the number of publications reporting the interaction, the reported interaction detection methods and interaction types.
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bau131-F2: The MIscore normalized score calculates a composite score for an interaction based on the number of publications reporting the interaction, the reported interaction detection methods and interaction types.

Mentions: By default MIscore presents a normalized score (SMI) between 0 and 1 reflecting the reliability of its combined experimental evidence. This score is calculated from the weighted sum of the three different sub-scores listed above: number of publications (p), experimental detection methods (m) and interaction types (t) found for the interaction (Figure 2). The importance of each variable in the main equation can be adjusted using a weight factor. Each of these sub-scores is also represented by a score between 0 and 1.SMI=Kp×Sp(n)+Km×Sm(cv)+Kt×St(cv)Kp+Km+KtK[p,m,t]≡Weight factor  //  K∈[0−1]S[p,m,t]≡Scores  //  S ∈[0−1] Figure 2.


Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study.

Villaveces JM, Jiménez RC, Porras P, Del-Toro N, Duesbury M, Dumousseau M, Orchard S, Choi H, Ping P, Zong NC, Askenazi M, Habermann BH, Hermjakob H - Database (Oxford) (2015)

The MIscore normalized score calculates a composite score for an interaction based on the number of publications reporting the interaction, the reported interaction detection methods and interaction types.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4316181&req=5

bau131-F2: The MIscore normalized score calculates a composite score for an interaction based on the number of publications reporting the interaction, the reported interaction detection methods and interaction types.
Mentions: By default MIscore presents a normalized score (SMI) between 0 and 1 reflecting the reliability of its combined experimental evidence. This score is calculated from the weighted sum of the three different sub-scores listed above: number of publications (p), experimental detection methods (m) and interaction types (t) found for the interaction (Figure 2). The importance of each variable in the main equation can be adjusted using a weight factor. Each of these sub-scores is also represented by a score between 0 and 1.SMI=Kp×Sp(n)+Km×Sm(cv)+Kt×St(cv)Kp+Km+KtK[p,m,t]≡Weight factor  //  K∈[0−1]S[p,m,t]≡Scores  //  S ∈[0−1] Figure 2.

Bottom Line: The evidence that two molecules interact in a living cell is often inferred from multiple different experiments.We have developed both a merging algorithm and a scoring system for molecular interactions based on the proteomics standard initiative-molecular interaction standards.In this manuscript, we introduce these two algorithms and provide community access to the tool suite, describe examples of how these tools are useful to selectively present molecular interaction data and demonstrate a case where the algorithms were successfully used to identify a systematic error in an existing dataset.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Matinsried, Germany, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Department of Physiology and Department of Medicine, Division of Cardiology, David Geffen School of Medicine at UCLA, 675 Charles E. Young Drive, MRL Building, Suite 1609, Los Angeles, California 90095, USA and Biomedical Hosting LLC, Arlington, Massachusetts 02474, USA.

Show MeSH