ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2018
The gene regulatory network of mESC differentiation: a benchmark for reverse engineering methods.
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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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
3 citing papers in PubMed.
- Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data.NPJ systems biology and applications · 2024Article
- Robust network inference using response logic.Bioinformatics (Oxford, England) · 2019Article
- Designer human tissue: coming to a lab near you.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018Article
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Authors and funding
2 authors.
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Abstract
A large body of data have accumulated that characterize the gene regulatory network of stem cells. Yet, a comprehensive and integrative understanding of this complex network is lacking. Network reverse engineering methods that use transcriptome data to derive these networks may help to uncover the topology in an unbiased way. Many methods exist that use co-expression to reconstruct networks. However, it remains unclear how these methods perform in the context of stem cell differentiation, as most systematic assessments have been made for regulatory networks of unicellular organisms. Here, we report a systematic benchmark of different reverse engineering methods against functional data. We show that network pruning is critical for reconstruction performance. We also find that performance is similar for algorithms that use different co-expression measures, i.e. mutual information or correlation. In addition, different methods yield very different network topologies, highlighting the challenge of interpreting these resulting networks as a whole.This article is part of the theme issue 'Designer human tissue: coming to a lab near you'.
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