ArticleScientific reports2016
Gene regulatory network inference using fused LASSO on multiple data sets.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- DataXflowGen for GenAI-driven model generation.Scientific reports · 2026Article
- DeepCE: a deep learning framework for correlation-enhanced gene regulatory network inference in single-cell RNA sequencing data.Bioinformatics advances · 2026Article
- BCTI: a Bayesian network-based method for revealing critical transitions in complex biological systems.PeerJ · 2026Article
- Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene coexpression networks.Genome research · 2025Article
- Finding key genes (UBE2T, KIF4A, CDCA3, and CDCA5) co-expressed in hepatitis, cirrhosis and hepatocellular carcinoma based on multiple bioinformatics techniques.BMC gastroenterology · 2024Article
- Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning.BMC medical informatics and decision making · 2024Article
- Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms.International journal of chronic obstructive pulmonary disease · 2024Article
- Article
- Integrative genetic and genomic networks identify microRNA associated with COPD and ILD.Scientific reports · 2023Article
- Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.PLoS computational biology · 2023Article
- Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.PLoS computational biology · 2023Article
- Widening the landscape of transcriptional regulation of green algal photoprotection.Nature communications · 2023Article
- Structure learning for gene regulatory networks.PLoS computational biology · 2023Article
- Using single cell atlas data to reconstruct regulatory networks.Nucleic acids research · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- Identification of hub genes and their correlation with immune infiltration in coronary artery disease through bioinformatics and machine learning methods.Journal of thoracic disease · 2022Article
- Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence.Cancers · 2021Review
- Inferring and analyzing gene regulatory networks from multi-factorial expression data: a complete and interactive suite.BMC genomics · 2021Article
- Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways.PLoS computational biology · 2021Article
- Geometric graphs from data to aid classification tasks with Graph Convolutional Networks.Patterns (New York, N.Y.) · 2021Article
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4 authors.
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Abstract
Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions.
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