ArticleNucleic acids research2012
Reconstructing dynamic gene regulatory networks from sample-based transcriptional data.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 30 citations in OpenAlex.
- RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique.BMC bioinformatics · 2022Article
- RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling.Communications biology · 2022Article
- Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer.Frontiers in oncology · 2022Review
- An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests.Scientific reports · 2021Article
- Mechanistic Modeling of Gene Regulation and Metabolism Identifies Potential Targets for Hepatocellular Carcinoma.Frontiers in genetics · 2020Article
- An integrative method to decode regulatory logics in gene transcription.Nature communications · 2017Article
- Rising Strengths Hong Kong SAR in Bioinformatics.Interdisciplinary sciences, computational life sciences · 2017Review
- Inference of Gene Regulatory Network Based on Local Bayesian Networks.PLoS computational biology · 2016Article
- Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks.Nucleic acids research · 2015Article
- Big biological data: challenges and opportunities.Genomics, proteomics & bioinformatics · 2014Article
- Drug repositioning framework by incorporating functional information.IET systems biology · 2013Article
- Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information.Nucleic acids research · 2013Article
- Investigation of anti-cancer mechanisms by comparative analysis of naked mole rat and rat.BMC systems biology · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current method for reconstructing gene regulatory networks faces a dilemma concerning the study of bio-medical problems. On the one hand, static approaches assume that genes are expressed in a steady state and thus cannot exploit and describe the dynamic patterns of an evolving process. On the other hand, approaches that can describe the dynamical behaviours require time-course data, which are normally not available in many bio-medical studies. To overcome the limitations of both the static and dynamic approaches, we propose a dynamic cascaded method (DCM) to reconstruct dynamic gene networks from sample-based transcriptional data. Our method is based on the intra-stage steady-rate assumption and the continuity assumption, which can properly characterize the dynamic and continuous nature of gene transcription in a biological process. Our simulation study showed that compared with static approaches, the DCM not only can reconstruct dynamical network but also can significantly improve network inference performance. We further applied our method to reconstruct the dynamic gene networks of hepatocellular carcinoma (HCC) progression. The derived HCC networks were verified by functional analysis and network enrichment analysis. Furthermore, it was shown that the modularity and network rewiring in the HCC networks can clearly characterize the dynamic patterns of HCC progression.
Indexed as
Identifiers
What OpenQuestion holds
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.