ArticleScientific reports2021
KBoost: a new method to infer gene regulatory networks from gene expression data.
Article in Scientific reports, 2021. 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.
- Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.Methods in molecular biology (Clifton, N.J.) · 2027Review
- SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.BMC bioinformatics · 2026Article
- BCTI: a Bayesian network-based method for revealing critical transitions in complex biological systems.PeerJ · 2026Article
- MulNet: a scalable framework for reconstructing intra- and intercellular signaling networks from bulk and single-cell RNA-seq data.Briefings in bioinformatics · 2025Article
- Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions inFrontiers in microbiology · 2025Article
- Augusta: From RNA-Seq to gene regulatory networks and Boolean models.Computational and structural biotechnology journal · 2024Article
- Machine Learning Inference of Gene Regulatory Networks in DevelopingPlants (Basel, Switzerland) · 2024Article
- Development and Validation of an AI-Driven System for Automatic Literature Analysis and Molecular Regulatory Network Construction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Deciphering lineage-relevant gene regulatory networks during endoderm formation by InPheRNo-ChIP.Briefings in bioinformatics · 2024Article
- Analysis of translesion polymerases in colorectal cancer cells following cetuximab treatment: A network perspective.Cancer medicine · 2024Article
- Gene essentiality in cancer is better predicted by mRNA abundance than by gene regulatory network-inferred activity.NAR cancer · 2023Article
- Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.PLoS computational biology · 2023Article
- Generation of Realistic Gene Regulatory Networks by Enriching for Feed-Forward Loops.Frontiers in genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.
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.