ArticleGenome biology2016
The Local Edge Machine: inference of dynamic models of gene regulation.
Article in Genome biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Genetic network structure and dynamics: identifying simple negative feedback loops.Interface focus · 2025Article
- Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.PLoS computational biology · 2023Article
- A general model-based causal inference method overcomes the curse of synchrony and indirect effect.Nature communications · 2023Article
- Experimental guidance for discovering genetic networks through hypothesis reduction on time series.PLoS computational biology · 2022Article
- RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique.BMC bioinformatics · 2022Article
- Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes.BMC bioinformatics · 2022Article
- Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline.Journal of visualized experiments : JoVE · 2021Article
- Article
- Warm nights disrupt transcriptome rhythms in field-grown rice panicles.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Using extremal events to characterize noisy time series.Journal of mathematical biology · 2020Article
- Layers of regulation of cell-cycle gene expression in the budding yeast Saccharomyces cerevisiae.Molecular biology of the cell · 2018Article
- Reconciling conflicting models for global control of cell-cycle transcription.Cell cycle (Georgetown, Tex.) · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle. Finally, we use an atlas of transcription data in a mammalian circadian system to illustrate how the method can be used for discovery in the context of large complex networks.
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.