ArticleBMC bioinformatics2017
A scalable algorithm for structure identification of complex gene regulatory network from temporal expression data.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 6 citations in OpenAlex.
- Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.PLoS computational biology · 2023Article
- Time-invariant biological networks with feedback loops: structural equation models and structural identifiability.IET systems biology · 2018Article
- Hierarchical parameter estimation of GRN based on topological analysis.IET systems biology · 2018Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundGene regulatory interactions are of fundamental importance to various biological functions and processes. However, only a few previous computational studies have claimed success in revealing genome-wide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human. Moreover, recent work suggests that these methods still suffer from the curse of dimensionality if a network size increases to 100 or higher.
resultsHere we present a novel scalable algorithm for identifying genome-wide gene regulatory network (GRN) structures, and we have verified the algorithm performances by extensive simulation studies based on the DREAM challenge benchmark data. The highlight of our method is that its superior performance does not degenerate even for a network size on the order of 10
conclusionsThe proposed algorithm is the first scalable method for large complex network structure identification. The GRN structure identified by our algorithm could reveal possible biological links and help researchers to choose which gene functions to investigate in a biological event. The algorithm described in this article is implemented in MATLAB
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