ArticlePloS one2014
Profiling animal toxicants by automatically mining public bioassay data: a big data approach for computational toxicology.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 40 citations in OpenAlex.
- Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways.Environmental science & technology · 2023Article
- Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data.Environmental science & technology · 2022Article
- The Toxicity of Wiped Dust and Airborne Microbes in Individual Classrooms Increase the Risk of Teachers' Work-Related Symptoms: A Cross-Sectional Study.Pathogens (Basel, Switzerland) · 2021Article
- Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids.ACS sustainable chemistry & engineering · 2021Article
- Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.Drug discovery today · 2020Review
- Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement.International journal of molecular sciences · 2020Review
- Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
- Analysis of model PMEcotoxicology and environmental safety · 2020Article
- Big Data and Artificial Intelligence Modeling for Drug Discovery.Annual review of pharmacology and toxicology · 2020Review
- Identifying Compounds with Genotoxicity Potential Using Tox21 High-Throughput Screening Assays.Chemical research in toxicology · 2019Article
- Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity.Chemical research in toxicology · 2019Review
- Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across.Environmental health perspectives · 2019Article
- Big-data and machine learning to revamp computational toxicology and its use in risk assessment.Toxicology research · 2018Review
- Alternative approaches for identifying acute systemic toxicity: Moving from research to regulatory testing.Toxicology in vitro : an international journal published in association with BIBRA · 2017Review
- CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data.Bioinformatics (Oxford, England) · 2017Article
- Real-time cell toxicity profiling of Tox21 10K compounds reveals cytotoxicity dependent toxicity pathway linkage.PloS one · 2017Article
- Getting the most out of PubChem for virtual screening.Expert opinion on drug discovery · 2016Review
- Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data.Environmental health perspectives · 2016Article
- Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data.Frontiers in environmental science · 2016Article
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 at 2 institutions in 1 country.
Funding
Abstract
In vitro bioassays have been developed and are currently being evaluated as potential alternatives to traditional animal toxicity models. Already, the progress of high throughput screening techniques has resulted in an enormous amount of publicly available bioassay data having been generated for a large collection of compounds. When a compound is tested using a collection of various bioassays, all the testing results can be considered as providing a unique bio-profile for this compound, which records the responses induced when the compound interacts with different cellular systems or biological targets. Profiling compounds of environmental or pharmaceutical interest using useful toxicity bioassay data is a promising method to study complex animal toxicity. In this study, we developed an automatic virtual profiling tool to evaluate potential animal toxicants. First, we automatically acquired all PubChem bioassay data for a set of 4,841 compounds with publicly available rat acute toxicity results. Next, we developed a scoring system to evaluate the relevance between these extracted bioassays and animal acute toxicity. Finally, the top ranked bioassays were selected to profile the compounds of interest. The resulting response profiles proved to be useful to prioritize untested compounds for their animal toxicity potentials and form a potential in vitro toxicity testing panel. The protocol developed in this study could be combined with structure-activity approaches and used to explore additional publicly available bioassay datasets for modeling a broader range of animal toxicities.
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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.