ArticlePloS one2013
Comparison of profile similarity measures for genetic interaction networks.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The Microbial Network Stability in Cyanobacterial and Moss Biocrusts Respond Differently to Climate Warming.Microorganisms · 2026Article
- Soil fungal community has higher network stability than bacterial community in response to warming and nitrogen addition in a subtropical primary forest.Applied and environmental microbiology · 2024Article
- Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions.Journal of chemical information and modeling · 2021Article
- Measuring similarity between gene interaction profiles.BMC bioinformatics · 2019Article
- Article
- Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions.PLoS computational biology · 2018Article
- Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets.PLoS computational biology · 2018Article
- Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles.PloS one · 2018Article
- Detection of statistically significant network changes in complex biological networks.BMC systems biology · 2017Article
- A global genetic interaction network maps a wiring diagram of cellular function.Science (New York, N.Y.) · 2016Article
- A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data.PloS one · 2015Article
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Authors and funding
3 authors.
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Abstract
Analysis of genetic interaction networks often involves identifying genes with similar profiles, which is typically indicative of a common function. While several profile similarity measures have been applied in this context, they have never been systematically benchmarked. We compared a diverse set of correlation measures, including measures commonly used by the genetic interaction community as well as several other candidate measures, by assessing their utility in extracting functional information from genetic interaction data. We find that the dot product, one of the simplest vector operations, outperforms most other measures over a large range of gene pairs. More generally, linear similarity measures such as the dot product, Pearson correlation or cosine similarity perform better than set overlap measures such as Jaccard coefficient. Similarity measures that involve L2-normalization of the profiles tend to perform better for the top-most similar pairs but perform less favorably when a larger set of gene pairs is considered or when the genetic interaction data is thresholded. Such measures are also less robust to the presence of noise and batch effects in the genetic interaction data. Overall, the dot product measure performs consistently among the best measures under a variety of different conditions and genetic interaction datasets.
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