ArticleBioinformatics (Oxford, England)2014
Estimating the activity of transcription factors by the effect on their target genes.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
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- Gene regulatory networks in disease and ageing.Nature reviews. Nephrology · 2024Review
- The Blueprint of Logical Decisions in a NF-κB Signaling System.ACS omega · 2024Article
- Network-based drug repurposing for schizophrenia.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Article
- Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.Genome biology · 2024Article
- A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data.NAR genomics and bioinformatics · 2023Article
- Identifying genetic regulatory variants that affect transcription factor activity.Cell genomics · 2023Article
- Characterization of cell-fate decision landscapes by estimating transcription factor dynamics.Cell reports methods · 2023Article
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- Alternative splicing: transcriptional regulatory network in agroforestry.Frontiers in plant science · 2023Review
- Coordinated control of neuronal differentiation and wiring by sustained transcription factors.Science (New York, N.Y.) · 2022Article
- Systems-level identification of key transcription factors in immune cell specification.PLoS computational biology · 2022Article
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- Profiling transcription factor activity dynamics using intronic reads in time-series transcriptome data.PLoS computational biology · 2022Article
- Taiji-reprogram: a framework to uncover cell-type specific regulators and predict cellular reprogramming cocktails.NAR genomics and bioinformatics · 2021Article
- Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data.Bioinformatics (Oxford, England) · 2021Article
- Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources.BMC bioinformatics · 2021Article
- Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges.Cancers · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationUnderstanding regulation of transcription is central for elucidating cellular regulation. Several statistical and mechanistic models have come up the last couple of years explaining gene transcription levels using information of potential transcriptional regulators as transcription factors (TFs) and information from epigenetic modifications. The activity of TFs is often inferred by their transcription levels, promoter binding and epigenetic effects. However, in principle, these methods do not take hard-to-measure influences such as post-transcriptional modifications into account.
resultsFor TFs, we present a novel concept circumventing this problem. We estimate the regulatory activity of TFs using their cumulative effects on their target genes. We established our model using expression data of 59 cell lines from the National Cancer Institute. The trained model was applied to an independent expression dataset of melanoma cells yielding excellent expression predictions and elucidated regulation of melanogenesis. AVAILABILITY AND IMPLEMENTATION: Using mixed-integer linear programming, we implemented a switch-like optimization enabling a constrained but optimal selection of TFs and optimal model selection estimating their effects. The method is generic and can also be applied to further regulators of transcription. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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