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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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MIscore and Mentha true-positive rates vs. the false-positive rates for different score cutoffs.
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bau131-F3: MIscore and Mentha true-positive rates vs. the false-positive rates for different score cutoffs.

Mentions: Using the datasets described above, true positive and false positive rates were calculated for different cutoffs and then plotted (Figure 3). The figure suggests that MIscore and Mentha perform similarly since ROC curves have comparable area under the curve (AUC). The Mentha ROC curve rises steeply, which is consistent with higher precision. However, the MIscore ROC recovers at the end.Figure 3.


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)

MIscore and Mentha true-positive rates vs. the false-positive rates for different score cutoffs.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

bau131-F3: MIscore and Mentha true-positive rates vs. the false-positive rates for different score cutoffs.
Mentions: Using the datasets described above, true positive and false positive rates were calculated for different cutoffs and then plotted (Figure 3). The figure suggests that MIscore and Mentha perform similarly since ROC curves have comparable area under the curve (AUC). The Mentha ROC curve rises steeply, which is consistent with higher precision. However, the MIscore ROC recovers at the end.Figure 3.

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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