ArticleBMC systems biology2017
Estimating drivers of cell state transitions using gene regulatory network models.
Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Genetic Modulation of Neurocognitive Development in Cancer Patients throughout the Lifespan: a Systematic Review.Neuropsychology review · 2019Pooled it
- Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations.Nature communications · 2019Pooled it
- Gene regulatory network analysis identifies dysregulation of hypoxia pathways as contributing to glioblastoma treatment resistance in females.Biology of sex differences · 2026Article
- Gene regulatory network analysis identifies dysregulation of hypoxia pathways as contributing to glioblastoma multiforme treatment resistance in females.medRxiv : the preprint server for health sciences · 2026Article
- Higher-order correction of persistent batch effects in correlation networks.Bioinformatics (Oxford, England) · 2024Article
- Predicting anti-cancer drug combination responses with a temporal cell state network model.PLoS computational biology · 2023Article
- The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.Genome biology · 2023Article
- Allelic correlation is a marker of trade-offs between barriers to transmission of expression variability and signal responsiveness in genetic networks.Cell systems · 2022Article
- Altered transcription factor targeting is associated with differential peripheral blood mononuclear cell proportions in sarcoidosis.Frontiers in immunology · 2022Article
- Gene Networks with Transcriptional Bursting Recapitulate Rare Transient Coordinated High Expression States in Cancer.Cell systems · 2020Article
- Estimating Sample-Specific Regulatory Networks.iScience · 2019Article
- Predicting growth rate from gene expression.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundSpecific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks.
resultsHere we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state.
conclusionsWe demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.
Indexed as
Identifiers
What OpenQuestion holds
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.