ArticleBMC bioinformatics2014
Exact reconstruction of gene regulatory networks using compressive sensing.
Article in BMC bioinformatics, 2014. 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, 45 citations in OpenAlex.
- Robust Model-Free Identification of the Causal Networks Underlying Complex Nonlinear Systems.Entropy (Basel, Switzerland) · 2024Article
- Article
- The identifiability of gene regulatory networks: the role of observation data.Journal of biological physics · 2022Article
- Hierarchical parameter estimation of GRN based on topological analysis.IET systems biology · 2018Article
- Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality.Scientific reports · 2017Article
- Data-driven reverse engineering of signaling pathways using ensembles of dynamic models.PLoS computational biology · 2017Article
- Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem.Scientific reports · 2016Article
- Reconstructing Networks from Profit Sequences in Evolutionary Games via a Multiobjective Optimization Approach with Lasso Initialization.Scientific reports · 2016Article
- Semi-supervised prediction of gene regulatory networks using machine learning algorithms.Journal of biosciences · 2015Article
- Reconstruction of Gene Regulatory Networks based on Repairing Sparse Low-rank Matrices.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundWe consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network's sparseness.
resultsFor the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. We demonstrate that it is possible to use the coherence distribution to guide biological experiment design effectively. By collecting a more informative dataset, the proposed method helps reduce the cost of experiments. For each problem, a set of numerical examples is presented.
conclusionsThe method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies.
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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.