ArticleBioinformatics (Oxford, England)2015
The assembly of miRNA-mRNA-protein regulatory networks using high-throughput expression data.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- micro-RNAs dependent regulation of DNMT and HIF1α gene expression in thrombotic disorders.Scientific reports · 2019Pooled it
- RNA Network Interactions During Differentiation of Human Trophoblasts.Frontiers in cell and developmental biology · 2021Article
- Differential expression profile study and gene function analysis of maternal foetal-derived circRNA for screening for Down's syndrome.Experimental and therapeutic medicine · 2020Article
- Expression and trafficking of placental microRNAs at the feto-maternal interface.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2017Article
- Integration of MicroRNA, mRNA, and Protein Expression Data for the Identification of Cancer-Related MicroRNAs.PloS one · 2017Article
- Predicting miRNA Targets by Integrating Gene Regulatory Knowledge with Expression Profiles.PloS one · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationInference of gene regulatory networks from high throughput measurement of gene and protein expression is particularly attractive because it allows the simultaneous discovery of interactive molecular signals for numerous genes and proteins at a relatively low cost.
resultsWe developed two score-based local causal learning algorithms that utilized the Markov blanket search to identify direct regulators of target mRNAs and proteins. These two algorithms were specifically designed for integrated high throughput RNA and protein data. Simulation study showed that these algorithms outperformed other state-of-the-art gene regulatory network learning algorithms. We also generated integrated miRNA, mRNA, and protein expression data based on high throughput analysis of primary trophoblasts, derived from term human placenta and cultured under standard or hypoxic conditions. We applied the new algorithms to these data and identified gene regulatory networks for a set of trophoblastic proteins found to be differentially expressed under the specified culture conditions.
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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.