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Using networks to measure similarity between genes: association index selection

Nature Methods volume 10, pages 11691176 (2013) | Download Citation

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  • A Corrigendum to this article was published on 27 February 2014

This article has been updated

Abstract

Biological networks can be used to functionally annotate genes on the basis of interaction-profile similarities. Metrics known as association indices can be used to quantify interaction-profile similarity. We provide an overview of commonly used association indices, including the Jaccard index and the Pearson correlation coefficient, and compare their performance in different types of analyses of biological networks. We introduce the Guide for Association Index for Networks (GAIN), a web tool for calculating and comparing interaction-profile similarities and defining modules of genes with similar profiles.

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Change history

  • 27 January 2014

    In the version of this article initially published, the formula describing the connection specificity index (CSI) in Box 2 was incorrect. The denominator in the fraction of the CSI equation originally read "ny"; the correct denominator is "# of X-type nodes in the network." The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

We thank members of A.J.M.W.'s lab, R. McCord and B. Lajoie for discussions and critical reading of the manuscript. We thank J.C. Bare (Institute of Systems Biology) for helpful advice in the development of GAIN. This work was supported by the US National Institutes of Health grants DK068429 and GM082971 to A.J.M.W. J.I.F.B. is partially supported by a postdoctoral fellowship from the Pew Latin American Fellows Program. J.N. and C.L.M. are partially supported by grant DBI-0953881 from the US National Science Foundation.

Author information

Affiliations

  1. Program in Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts, USA.

    • Juan I Fuxman Bass
    • , Alos Diallo
    • , Juan M Soto
    •  & Albertha J M Walhout
  2. Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.

    • Juan I Fuxman Bass
    • , Alos Diallo
    • , Juan M Soto
    •  & Albertha J M Walhout
  3. Department of Computer Science and Engineering, University of Minnesota–Twin Cities, Minneapolis, Minnesota, USA.

    • Justin Nelson
    •  & Chad L Myers

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Contributions

J.I.F.B. and A.J.M.W. conceived the project; J.I.F.B. performed the data analysis with the assistance of A.D., J.N. and C.L.M.; A.D. and J.I.F.B. developed the GAIN web tool in collaboration with J.N., C.L.M. and J.M.S.; J.I.F.B. and A.J.M.W. wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Juan I Fuxman Bass or Albertha J M Walhout.

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    Supplementary Text and Figures

    Supplementary Figures 1–3, Supplementary Table 1 and Supplementary Methods

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DOI

https://doi.org/10.1038/nmeth.2728

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