ArticlePloS one2010
Towards a rigorous assessment of systems biology models: the DREAM3 challenges.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 187 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
187 citing papers in PubMed, 1 synthesis or guideline pooled it, 447 citations in OpenAlex.
- Gene regulatory network reconstruction using Bayesian networks, the Dantzig Selector, the Lasso and their meta-analysis.PloS one · 2011Pooled it
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- GeneSNAKE: a Python package for simulation of gene regulatory networks and perturbation-induced expression data.Bioinformatics advances · 2026Article
- MultiCausGRN: directed prior-guided graph attention model for multi-omics gene regulatory network inference.Frontiers in bioinformatics · 2026Article
- Multi-omic network inference from time-series data.NPJ systems biology and applications · 2025Article
- Critical assessment of the ability of Boolean threshold models to describe gene regulatory network dynamics.PNAS nexus · 2025Article
- BiGSM: Bayesian inference of gene regulatory network via sparse modelling.Bioinformatics (Oxford, England) · 2025Article
- Determining interaction directionality in complex biochemical networks from stationary measurements.Scientific reports · 2025Article
- Leveraging dynamic stability to infer regulation in protein-protein interaction networks: A study of infectious vulnerability in COPD.PloS one · 2025Article
- Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data.BMC biology · 2024Article
- Inference of gene regulatory networks based on directed graph convolutional networks.Briefings in bioinformatics · 2024Article
- Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2024Article
- A Noise-Tolerating Gene Association Network Uncovering an Oncogenic Regulatory Motif in Lymphoma Transcriptomics.Life (Basel, Switzerland) · 2023Article
- EnsInfer: a simple ensemble approach to network inference outperforms any single method.BMC bioinformatics · 2023Article
- Gene regulation network inference using k-nearest neighbor-based mutual information estimation: revisiting an old DREAM.BMC bioinformatics · 2023Article
- Article
127 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 7 institutions in 4 countries.
Funding
Abstract
backgroundSystems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. METHODOLOGY AND PRINCIPAL
findingsWe describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method.
conclusionsDREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature.
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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.