ReviewTrends in genetics : TIG2016
Past Roadblocks and New Opportunities in Transcription Factor Network Mapping.
Review in Trends in genetics : TIG, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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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Who cites it
18 citing papers in PubMed.
- Article
- Transcription factor networks in cellular quiescence.Nature cell biology · 2025Review
- Computational Reconstruction of the Transcription Factor Regulatory Network Induced by Auxin inPlants (Basel, Switzerland) · 2024Article
- NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration.Bioinformatics (Oxford, England) · 2023Article
- The MITF regulatory network in melanoma.Pigment cell & melanoma research · 2022Article
- TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data.Nucleic acids research · 2021Article
- Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data.Bioinformatics (Oxford, England) · 2021Article
- Dynamic proteomic profiling of human periodontal ligament stem cells during osteogenic differentiation.Stem cell research & therapy · 2021Article
- An Early Stage Researcher's Primer on Systems Medicine Terminology.Network and systems medicine · 2021Review
- Runx1 and Runx3 drive progenitor to T-lineage transcriptome conversion in mouse T cell commitment via dynamic genomic site switching.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- A Hierarchical Transcriptional Regulatory Network Required for Long-Term Thermal Stress Tolerance in an IndustrialFrontiers in bioengineering and biotechnology · 2021Article
- NetProphet 2.0: mapping transcription factor networks by exploiting scalable data resources.Bioinformatics (Oxford, England) · 2018Article
- The Genome-Scale Integrated Networks in Microorganisms.Frontiers in microbiology · 2018Review
- Genomic and molecular control of cell type and cell type conversions.Cell regeneration (London, England) · 2017Review
- Mammalian Transcription Factor Networks: Recent Advances in Interrogating Biological Complexity.Cell systems · 2017Review
- A cross-species approach to identify transcriptional regulators exemplified for Dnajc22 and Hnf4a.Scientific reports · 2017Article
- Article
- Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
One of the principal mechanisms by which cells differentiate and respond to changes in external signals or conditions is by changing the activity levels of transcription factors (TFs). This changes the transcription rates of target genes via the cell's TF network, which ultimately contributes to reconfiguring cellular state. Since microarrays provided our first window into global cellular state, computational biologists have eagerly attacked the problem of mapping TF networks, a key part of the cell's control circuitry. In retrospect, however, steady-state mRNA abundance levels were a poor substitute for TF activity levels and gene transcription rates. Likewise, mapping TF binding through chromatin immunoprecipitation proved less predictive of functional regulation and less amenable to systematic elucidation of complete networks than originally hoped. This review explains these roadblocks and the current, unprecedented blossoming of new experimental techniques built on second-generation sequencing, which hold out the promise of rapid progress in TF network mapping.
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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.