ArticleJournal of genetics2010
Reverse engineering large-scale genetic networks: synthetic versus real data.
Article in Journal of genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context.PloS one · 2012Article
- Identifying Tmem59 related gene regulatory network of mouse neural stem cell from a compendium of expression profiles.BMC systems biology · 2011Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Development of microarray technology has resulted in an exponential rise in gene expression data. Linear computational methods are of great assistance in identifying molecular interactions, and elucidating the functional properties of gene networks. It overcomes the weaknesses of in vivo experiments including high cost, large noise, and unrepeatable process. In this paper, we propose an easily applied system, Stepwise Network Inference (SWNI), which integrates deterministic linear model with statistical analysis, and has been tested effectively on both simulated experiments and real gene expression data sets. The study illustrates that connections of gene networks can be significantly detected via SWNI with high confidence, when single gene perturbation experiments are performed complying with the algorithm requirements. In particular, our algorithm shows efficiency and outperforms the existing ones presented in this paper when dealing with large-scale sparse networks without any prior knowledge.
Indexed as
Identifiers
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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.