ArticleNature communications2017
An integrative method to decode regulatory logics in gene transcription.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- Developing a novel aging assessment model to uncover heterogeneity in organ aging and screening of aging-related drugs.Genome medicine · 2025Article
- Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms.Briefings in bioinformatics · 2024Article
- Abundant binary promoter switches in lineage-determining transcription factors indicate a digital component of cell fate determination.Cell reports · 2023Article
- Integrated modeling framework reveals co-regulation of transcription factors, miRNAs and lncRNAs on cardiac developmental dynamics.Stem cell research & therapy · 2023Article
- RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique.BMC bioinformatics · 2022Article
- Three topological features of regulatory networks control life-essential and specialized subsystems.Scientific reports · 2021Article
- Review
- Identifying Transcription Factor Combinations to Modulate Circadian Rhythms by Leveraging Virtual Knockouts on Transcription Networks.iScience · 2020Article
- IGF2BP2 Polymorphisms Are Associated with Clinical Characteristics and Development of Oral Cancer.International journal of molecular sciences · 2020Article
- Understanding Quantitative Circadian Regulations Are Crucial Towards Advancing Chronotherapy.Cells · 2019Review
- Prediction of Gene Expression Patterns With Generalized Linear Regression Model.Frontiers in genetics · 2019Article
- Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data.Scientific reports · 2018Article
- Relationship betweenCancer management and research · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modeling of transcriptional regulatory networks (TRNs) has been increasingly used to dissect the nature of gene regulation. Inference of regulatory relationships among transcription factors (TFs) and genes, especially among multiple TFs, is still challenging. In this study, we introduced an integrative method, LogicTRN, to decode TF-TF interactions that form TF logics in regulating target genes. By combining cis-regulatory logics and transcriptional kinetics into one single model framework, LogicTRN can naturally integrate dynamic gene expression data and TF-DNA-binding signals in order to identify the TF logics and to reconstruct the underlying TRNs. We evaluated the newly developed methodology using simulation, comparison and application studies, and the results not only show their consistence with existing knowledge, but also demonstrate its ability to accurately reconstruct TRNs in biological complex systems.
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.