ArticleAdvances in bioinformatics2013
Reverse engineering sparse gene regulatory networks using cubature kalman filter and compressed sensing.
Article in Advances in bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 13 citations in OpenAlex.
- New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics.Frontiers in computational neuroscience · 2023Article
- Reverse engineering gene regulatory networks from measurement with missing values.EURASIP journal on bioinformatics & systems biology · 2016Article
- Exact reconstruction of gene regulatory networks using compressive sensing.BMC bioinformatics · 2014Article
- Regularized EM algorithm for sparse parameter estimation in nonlinear dynamic systems with application to gene regulatory network inference.EURASIP journal on bioinformatics & systems biology · 2014Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF) and Kalman filter (KF) techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.
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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.