title: Using networks to measure similarity between genes: association index selection.
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.
authors.affiliation: 1] Program in Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts, USA. [2] Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
authors.forename: Juan I
authors.initials: JI
authors.lastname: Fuxman Bass
authors.forename: Alos
authors.initials: A
authors.lastname: Diallo
authors.forename: Justin
authors.initials: J
authors.lastname: Nelson
authors.forename: Juan M
authors.initials: JM
authors.lastname: Soto
authors.forename: Chad L
authors.initials: CL
authors.lastname: Myers
authors.forename: Albertha J M
authors.initials: AJ
authors.lastname: Walhout
mesh_terms: D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D017173:Caenorhabditis elegans; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D005838:Genotype; D006801:Humans; D020407:Internet; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D011401:Promoter Regions, Genetic; D049490:Systems Biology / Q000379:methods*