ArticleScientific reports2022
Knowledge of the perturbation design is essential for accurate gene regulatory network inference.
Article in Scientific reports, 2022. 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.
- Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer.Scientific reports · 2026Article
- Article
- GeneSNAKE: a Python package for simulation of gene regulatory networks and perturbation-induced expression data.Bioinformatics advances · 2026Article
- BiGSM: Bayesian inference of gene regulatory network via sparse modelling.Bioinformatics (Oxford, England) · 2025Article
- Topology-based metrics for finding the optimal sparsity in gene regulatory network inference.Bioinformatics (Oxford, England) · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.Bioinformatics advances · 2025Article
- GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data.NAR genomics and bioinformatics · 2024Article
- SAILoR: Structure-Aware Inference of Logic Rules.PloS one · 2024Article
- RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations.Communications biology · 2023Article
- LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.Bioinformatics (Oxford, England) · 2023Article
- Improving gene regulatory network inference and assessment: The importance of using network structure.Frontiers in genetics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The gene regulatory network (GRN) of a cell executes genetic programs in response to environmental and internal cues. Two distinct classes of methods are used to infer regulatory interactions from gene expression: those that only use observed changes in gene expression, and those that use both the observed changes and the perturbation design, i.e. the targets used to cause the changes in gene expression. Considering that the GRN by definition converts input cues to changes in gene expression, it may be conjectured that the latter methods would yield more accurate inferences but this has not previously been investigated. To address this question, we evaluated a number of popular GRN inference methods that either use the perturbation design or not. For the evaluation we used targeted perturbation knockdown gene expression datasets with varying noise levels generated by two different packages, GeneNetWeaver and GeneSpider. The accuracy was evaluated on each dataset using a variety of measures. The results show that on all datasets, methods using the perturbation design matrix consistently and significantly outperform methods not using it. This was also found to be the case on a smaller experimental dataset from E. coli. Targeted gene perturbations combined with inference methods that use the perturbation design are indispensable for accurate GRN inference.
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.