ReviewBriefings in bioinformatics2012
Learning transcriptional regulation on a genome scale: a theoretical analysis based on gene expression data.
Review in Briefings in bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
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Who cites it
12 citing papers in PubMed.
- SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.Journal of statistical software · 2021Article
- Lessons from eRNAs: understanding transcriptional regulation through the lens of nascent RNAs.Transcription · 2020Review
- Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks.BMC systems biology · 2019Article
- Urate-lowering therapy alleviates atherosclerosis inflammatory response factors and neointimal lesions in a mouse model of induced carotid atherosclerosis.The FEBS journal · 2019Article
- Pan- and core- gene association networks: Integrative approaches to understanding biological regulation.PloS one · 2019Article
- How to Predict Molecular Interactions between Species?Frontiers in microbiology · 2016Review
- Data- and knowledge-based modeling of gene regulatory networks: an update.EXCLI journal · 2015Review
- Article
- A multi-layer inference approach to reconstruct condition-specific genes and their regulation.Bioinformatics (Oxford, England) · 2013Article
- Transcription factor-microRNA-target gene networks associated with ovarian cancer survival and recurrence.PloS one · 2013Article
- A model-based method for gene dependency measurement.PloS one · 2012Article
- Reverse engineering: a key component of systems biology to unravel global abiotic stress cross-talk.Frontiers in plant science · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The recent advent of high-throughput microarray data has enabled the global analysis of the transcriptome, driving the development and application of computational approaches to study transcriptional regulation on the genome scale, by reconstructing in silico the regulatory interactions of the gene network. Although there are many in-depth reviews of such 'reverse-engineering' methodologies, most have focused on the practical aspect of data mining, and few on the biological problem and the biological relevance of the methodology. Therefore, in this review, from a biological perspective, we used a set of yeast microarray data as a working example, to evaluate the fundamental assumptions implicit in associating transcription factor (TF)-target gene expression levels and estimating TFs' activity, and further explore cooperative models. Finally we confirm that the detailed transcription mechanism is overly-complex for expression data alone to reveal, nevertheless, future network reconstruction studies could benefit from the incorporation of context-specific information, the modeling of multiple layers of regulation (e.g. micro-RNA), or the development of approaches for context-dependent analysis, to uncover the mechanisms of gene regulation.
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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.