ArticleBMC systems biology2010
Inferring the conservative causal core of gene regulatory networks.
Article in BMC systems biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 1 of them a synthesis that pooled it.
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Who cites it
88 citing papers in PubMed, 1 synthesis or guideline pooled it, 193 citations in OpenAlex.
- Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models.Cell reports · 2020Pooled it
- GT-GRN: a graph transformer framework for enhanced gene regulatory network inference via multimodal embedding of expression data and existing network knowledge.Briefings in bioinformatics · 2025Article
- Topology-based metrics for finding the optimal sparsity in gene regulatory network inference.Bioinformatics (Oxford, England) · 2025Article
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- Hypergraph reconstruction from uncertain pairwise observations.Scientific reports · 2023Article
- Improving gene regulatory network inference and assessment: The importance of using network structure.Frontiers in genetics · 2023Article
- Distinct sets of olfactory receptors highly expressed in different human tissues evaluated by meta-transcriptome analysis: Association of OR10A6 in skin with keratinization.Frontiers in cell and developmental biology · 2023Article
- Review
- Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm.Interdisciplinary sciences, computational life sciences · 2022Article
- Current status and trends in forest genomics.Forestry research · 2022Review
- The Performance Comparison of Gene Co-expression Networks of Breast and Prostate Cancer using Different Selection Criteria.Interdisciplinary sciences, computational life sciences · 2021Article
- A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data.Bioinformatics (Oxford, England) · 2021Article
- SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.Journal of statistical software · 2021Article
- Functional Analysis of Immune Signature Genes in Th1* Memory Cells Links ISOC1 and Pyrimidine Metabolism to IFN-γ and IL-17 Production.Journal of immunology (Baltimore, Md. : 1950) · 2021Article
- Analyzing Neck Circumference as an Indicator of CPAP Treatment Response in Obstructive Sleep Apnea with Network Medicine.Diagnostics (Basel, Switzerland) · 2021Article
- Gene Co-Expression in Breast Cancer: A Matter of Distance.Frontiers in oncology · 2021Article
- HB-PLS: A statistical method for identifying biological process or pathway regulators by integrating Huber loss and Berhu penalty with partial least squares regression.Forestry research · 2021Article
- Data-Driven Computational Social Network Science: Predictive and Inferential Models for Web-Enabled Scientific Discoveries.Frontiers in big data · 2021Article
- FINET: Fast Inferring NETwork.BMC research notes · 2020Article
28 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.
resultsIn this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.
conclusionsFor systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.
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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.