ReviewChaos (Woodbury, N.Y.)2010
A systems-biology approach to modular genetic complexity.
Review in Chaos (Woodbury, N.Y.), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Genetic interactions derived from high-throughput phenotyping of 6589 yeast cell cycle mutants.NPJ systems biology and applications · 2020Article
- Compound stress response in stomatal closure: a mathematical model of ABA and ethylene interaction in guard cells.BMC systems biology · 2012Article
- Relations between the set-complexity and the structure of graphs and their sub-graphs.EURASIP journal on bioinformatics & systems biology · 2012Article
- Identification of dysfunctional modules and disease genes in congenital heart disease by a network-based approach.BMC genomics · 2011Article
- GoldenBraid: an iterative cloning system for standardized assembly of reusable genetic modules.PloS one · 2011Article
- Introduction to focus issue: genetic interactions.Chaos (Woodbury, N.Y.) · 2010Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Multiple high-throughput genetic interaction studies have provided substantial evidence of modularity in genetic interaction networks. However, the correspondence between these network modules and specific pathways of information flow is often ambiguous. Genetic interaction and molecular interaction analyses have not generated large-scale maps comprising multiple clearly delineated linear pathways. We seek to clarify the situation by discerning the difference between genetic modules and classical pathways. We review a method to optimize the discovery of biologically meaningful genetic modules based on a previously described context-dependent information measure to obtain maximally informative networks. We compare the results of this method with the established measures of network clustering and find that it balances global and local clustering information in networks. We further discuss the consequences for genetic interaction networks and propose a framework for the analysis of genetic modularity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.