ArticlePloS one2013
Inference of gene regulatory networks from genetic perturbations with linear regression model.
Article in PloS one, 2013. 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.
- Regulatory network topology and the genetic architecture of gene expression.Cell genomics · 2026Article
- Unraveling the COVID-19 Severity Hubs and Interplays in Inflammatory-Related RNA-Protein Networks.International journal of molecular sciences · 2025Article
- A Noise-Tolerating Gene Association Network Uncovering an Oncogenic Regulatory Motif in Lymphoma Transcriptomics.Life (Basel, Switzerland) · 2023Article
- Phosphoproteomics data-driven signalling network inference: Does it work?Computational and structural biotechnology journal · 2023Article
- Bayesian differential analysis of gene regulatory networks exploiting genetic perturbations.BMC bioinformatics · 2020Article
- Time-invariant biological networks with feedback loops: structural equation models and structural identifiability.IET systems biology · 2018Article
- Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems.Advances in bioinformatics · 2017Article
- Structural identifiability of cyclic graphical models of biological networks with latent variables.BMC systems biology · 2016Article
- A Review of Emerging Technologies for the Management of Diabetes Mellitus.IEEE transactions on bio-medical engineering · 2015Review
- Bayesian Lookahead Perturbation Policy for Inference of Regulatory Networks.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks.Proceedings of the ... American Control Conference. American Control ConferenceArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is an effective strategy to use both genetic perturbation data and gene expression data to infer regulatory networks that aims to improve the detection accuracy of the regulatory relationships among genes. Based on both types of data, the genetic regulatory networks can be accurately modeled by Structural Equation Modeling (SEM). In this paper, a linear regression (LR) model is formulated based on the SEM, and a novel iterative scheme using Bayesian inference is proposed to estimate the parameters of the LR model (LRBI). Comparative evaluations of LRBI with other two algorithms, the Adaptive Lasso (AL-Based) and the Sparsity-aware Maximum Likelihood (SML), are also presented. Simulations show that LRBI has significantly better performance than AL-Based, and overperforms SML in terms of power of detection. Applying the LRBI algorithm to experimental data, we inferred the interactions in a network of 35 yeast genes. An open-source program of the LRBI algorithm is freely available upon request.
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