Title
Using networks to measure similarity between genes: association index selection
UMMS Affiliation
Program in Systems Biology; Program in Molecular Medicine
Publication Date
2013-11-26
Document Type
Article
Disciplines
Computational Biology | Genetics and Genomics | Systems Biology
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.
DOI of Published Version
10.1038/nmeth.2728
Source
Fuxman Bass JI, Diallo A, Nelson J, Soto JM, Myers CL, Walhout AJ. Using networks to measure similarity between genes: association index selection. Nat Methods. 2013 Nov 26;10(12):1169-76. doi: 10.1038/nmeth.2728. Link to article on publisher's site
Journal/Book/Conference Title
Nature methods
Related Resources
PubMed ID
24296474
Repository Citation
Fuxman Bass, Juan I.; Diallo, Alos; Nelson, Justin; Soto, Juan M.; Myers, Chad L.; and Walhout, Albertha J. M., "Using networks to measure similarity between genes: association index selection" (2013). Program in Systems Biology Publications and Presentations. 34.
https://escholarship.umassmed.edu/sysbio_pubs/34