ArticleNature biotechnology2015
Prediction of human population responses to toxic compounds by a collaborative competition.
Article in Nature biotechnology, 2015. 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 56 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
56 citing papers in PubMed.
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- A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations.npj drug discovery · 2026Article
- Evaluating scientific confidence in the concordance of in vitro and in vivo protective points of departure.Regulatory toxicology and pharmacology : RTP · 2024Article
- Optimal linear ensemble of binary classifiers.Bioinformatics advances · 2024Article
- Big data in basic and translational cancer research.Nature reviews. Cancer · 2022Review
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- Model systems and organisms for addressing inter- and intra-species variability in risk assessment.Regulatory toxicology and pharmacology : RTP · 2022Review
- Machine Learning Applied to the Search for Nonlinear Features in Breeding Populations.Frontiers in artificial intelligence · 2022Article
- Crowdsourced mapping of unexplored target space of kinase inhibitors.Nature communications · 2021Article
- CSVS, a crowdsourcing database of the Spanish population genetic variability.Nucleic acids research · 2021Article
- Conditionally Reprogrammed Cells and Robotic High-Throughput Screening for Precision Cancer Therapy.Frontiers in oncology · 2021Review
- Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics.Nature methods · 2021Article
- Simplex representation of molecular structure as universal QSAR/QSPR tool.Structural chemistry · 2021Review
- Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines.Patterns (New York, N.Y.) · 2020Review
- QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction.Journal of cheminformatics · 2020Article
- Systematic Analysis of Environmental Chemicals That Dysregulate Critical Period Plasticity-Related Gene Expression Reveals Common Pathways That Mimic Immune Response to Pathogen.Neural plasticity · 2020Article
- Population-based toxicity screening in human induced pluripotent stem cell-derived cardiomyocytes.Toxicology and applied pharmacology · 2019Article
Corrections and comments
- Erratum issuedErratum: Prediction of human population responses to toxic compounds by a collaborative competition.2015 · Friend, Stephen; Dearry, Allen; Simeonov, Anton; Tice, Raymond R; Rusyn, Ivan; Wright, Fred A; Stolovitzky, Gustavo; Xie, Yang; and Saez-Rodriguez, Julio [Added]
Authors and funding
26 authors.
Funding
Abstract
The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.
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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.