ArticleBioinformatics (Oxford, England)2009
Benchmarking regulatory network reconstruction with GRENDEL.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation.BMC bioinformatics · 2022Article
- sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters.BMC bioinformatics · 2017Article
- Article
- Past Roadblocks and New Opportunities in Transcription Factor Network Mapping.Trends in genetics : TIG · 2016Review
- A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis.Frontiers in plant science · 2016Article
- BioPreDyn-bench: a suite of benchmark problems for dynamic modelling in systems biology.BMC systems biology · 2015Article
- Supervised, semi-supervised and unsupervised inference of gene regulatory networks.Briefings in bioinformatics · 2014Article
- ENNET: inferring large gene regulatory networks from expression data using gradient boosting.BMC systems biology · 2013Article
- DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models.PloS one · 2010Article
- Genome-wide inference of regulatory networks in Streptomyces coelicolor.BMC genomics · 2010Article
- Article
- The identification of informative genes from multiple datasets with increasing complexity.BMC bioinformatics · 2010Article
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Authors and funding
2 authors.
Funding
Abstract
motivationOver the past decade, the prospect of inferring networks of gene regulation from high-throughput experimental data has received a great deal of attention. In contrast to the massive effort that has gone into automated deconvolution of biological networks, relatively little effort has been invested in benchmarking the proposed algorithms. The rate at which new network inference methods are being proposed far outpaces our ability to objectively evaluate and compare them. This is largely due to a lack of fully understood biological networks to use as gold standards.
resultsWe have developed the most realistic system to date that generates synthetic regulatory networks for benchmarking reconstruction algorithms. The improved biological realism of our benchmark leads to conclusions about the relative accuracies of reconstruction algorithms that are significantly different from those obtained with A-BIOCHEM, an established in silico benchmark. AVAILABILITY: The synthetic benchmark utility and the specific benchmark networks that were used in our analyses are available at http://mblab.wustl.edu/software/grendel/.
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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.