ArticleMethods in molecular biology (Clifton, N.J.)2011
Imputing and predicting quantitative genetic interactions in epistatic MAPs.
Article in Methods in molecular biology (Clifton, N.J.), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Article
- Data Imputation in Epistatic MAPs by Network-Guided Matrix Completion.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Regulatory networks in retinal ischemia-reperfusion injury.BMC genetics · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Mapping epistatic (or genetic) interactions has emerged as an important network biology approach for establishing functional relationships among genes and proteins. Epistasis networks are complementary to physical protein interaction networks, providing valuable insight into both the function of individual genes and the overall wiring of the cell. A high-throughput method termed "epistatic mini array profiles" (E-MAPs) was recently developed in yeast to quantify alleviating or aggravating interactions between gene pairs. The typical output of an E-MAP experiment is a large symmetric matrix of interaction scores. One problem with this data is the large amount of missing values - interactions that cannot be measured during the high-throughput process or whose measurements were discarded due to quality filtering steps. These missing values can reduce the effectiveness of some data analysis techniques and prevent the use of others. Here, we discuss one solution to this problem, imputation using nearest neighbors, and give practical examples of the use of a freely available implementation of this method.
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