ArticleMicroarrays (Basel, Switzerland)2015
"Upstream Analysis": An Integrated Promoter-Pathway Analysis Approach to Causal Interpretation of Microarray Data.
Article in Microarrays (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed, 74 citations in OpenAlex.
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- Proteomic Analysis Identifies Multiple Mechanisms of 5-Fluorouracil-Induced Gut Mucositis in Mice.Cancers · 2024Article
- Bacterial sepsis causes more dramatic pathogenetic changes in the Th1 pathway than does viral (COVID-19) sepsis: a prospective observational study of whole blood transcriptomes.Virology journal · 2024Observational
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- Epigenome-Wide Changes in the Cell Layers of the Vein Wall When Exposing the Venous Endothelium to Oscillatory Shear Stress.Epigenomes · 2023Article
- Transcriptome-based analysis of human peripheral blood reveals regulators of immune response in different viral infections.Frontiers in immunology · 2023Article
- Substantiate a read-across hypothesis by using transcriptome data-A case study on volatile diketones.Frontiers in toxicology · 2023Article
- Alternative transcription start sites contribute to acute-stress-induced transcriptome response in human skeletal muscle.Human genomics · 2022Article
- Article
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- WINNER: A network biology tool for biomolecular characterization and prioritization.Frontiers in big data · 2022Article
- Expression dynamics of HAND1/2 in in vitro human cardiomyocyte differentiation.Stem cell reports · 2021Article
- Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer.Genome medicine · 2021Article
- Computational Identification of Master Regulators Influencing Trypanotolerance in Cattle.International journal of molecular sciences · 2021Article
- A computational analysis of transcriptional profiles from CD8(+) T lymphocytes reveals potential mechanisms of HIV/AIDS control and progression.Computational and structural biotechnology journal · 2021Article
- Gene Expression Pattern of Peyer's Patch Lymphocytes Exposed to Kagocel Suggests Pattern-Recognition Receptors Mediate Its Action.Frontiers in pharmacology · 2021Article
- IGFBP2 Is a Potential Master Regulator Driving the Dysregulated Gene Network Responsible for Short Survival in Glioblastoma Multiforme.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A strategy is presented that allows a causal analysis of co-expressed genes, which may be subject to common regulatory influences. A state-of-the-art promoter analysis for potential transcription factor (TF) binding sites in combination with a knowledge-based analysis of the upstream pathway that control the activity of these TFs is shown to lead to hypothetical master regulators. This strategy was implemented as a workflow in a comprehensive bioinformatic software platform. We applied this workflow to gene sets that were identified by a novel triclustering algorithm in naphthalene-induced gene expression signatures of murine liver and lung tissue. As a result, tissue-specific master regulators were identified that are known to be linked with tumorigenic and apoptotic processes. To our knowledge, this is the first time that genes of expression triclusters were used to identify upstream regulators.
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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.