ArticlePeerJ2013
Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors.
Article in PeerJ, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Gene regulatory networks in disease and ageing.Nature reviews. Nephrology · 2024Review
- Psychiatry and sensation: the epigenetic links.Epigenomics · 2024Review
- Harnessing the biological complexity of Big Data from LINCS gene expression signatures.PloS one · 2018Article
- Integration of heterogeneous molecular networks to unravel gene-regulation in Mycobacterium tuberculosis.BMC systems biology · 2014Article
- Article
- B-cell lymphoma gene regulatory networks: biological consistency among inference methods.Frontiers in genetics · 2013Article
- Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too.Frontiers in genetics · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.
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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.