ArticleBMC bioinformatics2014
Inference of radio-responsive gene regulatory networks using the graphical lasso algorithm.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Radiomic clustering using graph network techniques coupled with unbalanced optimal transport.Computational and structural biotechnology journal · 2025Article
- Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods.International journal of molecular sciences · 2022Article
- Reproducibility of radiomic features using network analysis and its application in WassersteinJournal of medical imaging (Bellingham, Wash.) · 2021Article
- Bayesian differential analysis of gene regulatory networks exploiting genetic perturbations.BMC bioinformatics · 2020Article
- HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity.Frontiers in genetics · 2020Article
- A Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-seq Data.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- Discriminating cancer-related and cancer-unrelated chemoradiation-response genes for locally advanced rectal cancers.Scientific reports · 2016Article
- Integration of multi-omics data for integrative gene regulatory network inference.International journal of data mining and bioinformaticsArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInference of gene regulatory networks (GRNs) from gene microarray expression data is of great interest and remains a challenging task in systems biology. Despite many efforts to develop efficient computational methods, the successful modeling of GRNs thus far has been quite limited. To tackle this problem, we propose a novel framework to reconstruct radio-responsive GRNs based on the graphical lasso algorithm. In our attempt to study radiosensitivity, we reviewed the literature and analyzed two publicly available gene microarray datasets. The graphical lasso algorithm was applied to expression measurements for genes commonly found to be significant in these different analyses.
resultsAssuming that a protein-protein interaction network obtained from a reliable pathway database is a gold-standard network, a comparison between the networks estimated by the graphical lasso algorithm and the gold-standard network was performed. Statistically significant p-values were achieved when comparing the gold-standard network with networks estimated from one microarray dataset and when comparing the networks estimated from two microarray datasets.
conclusionOur results show the potential to identify new interactions between genes that are not present in a curated database and GRNs using microarray datasets via the graphical lasso algorithm.
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