ReviewChemical research in toxicology2014
Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants.
Review in Chemical research in toxicology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 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
58 citing papers in PubMed, 151 citations in OpenAlex.
- Hierarchical Mechanistic Modeling of Complex Toxicity Endpoints from Public Concentration-Response Data.Environmental science & technology · 2026Article
- Predicting cardiotoxicity in drug development: A deep learning approach.Journal of pharmaceutical analysis · 2025Article
- Revisiting the approaches to DNA damage detection in genetic toxicology: insights and regulatory implications.BioData mining · 2025Article
- Prediction and Prioritisation of Novel Anthelmintic Candidates from Public Databases Using Deep Learning and Available Bioactivity Data Sets.International journal of molecular sciences · 2025Article
- Impact of Exposure to Leaves From Metal-Polluted Sites on the Developmental Parameters of Larvae of the Dark Sword-Grass, Agrotis ipsilon (Lepidoptera: Noctuidae).Archives of environmental contamination and toxicology · 2024Article
- Advancing Toxicity Predictions: A Review onEnvironment & health (Washington, D.C.) · 2024Review
- The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications.Communications chemistry · 2024Article
- Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity.Journal of hazardous materials · 2024Article
- Grouping strategies for assessing and managing persistent and mobile substances.Environmental sciences Europe · 2024Review
- A mode of action-based probabilistic framework of dose-response assessment for nonmutagenic liver carcinogens: a case study of PCB-126.Toxicological sciences : an official journal of the Society of Toxicology · 2023Article
- Article
- Comparative analysis between zebrafish and an automated live-cell assay to classify developmental neurotoxicant chemicals.Toxicology and applied pharmacology · 2023Article
- Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways.Environmental science & technology · 2023Article
- Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure.Environmental science & technology · 2023Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Identification of nonmonotonic concentration-responses in Tox21 high-throughput screening estrogen receptor assays.Toxicology and applied pharmacology · 2022Article
- Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay.Journal of hazardous materials · 2022Article
- Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data.Environmental science & technology · 2022Article
- Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach.Environmental science & technology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
High-throughput screening (HTS) assays that measure the in vitro toxicity of environmental compounds have been widely applied as an alternative to in vivo animal tests of chemical toxicity. Current HTS studies provide the community with rich toxicology information that has the potential to be integrated into toxicity research. The available in vitro toxicity data is updated daily in structured formats (e.g., deposited into PubChem and other data-sharing web portals) or in an unstructured way (papers, laboratory reports, toxicity Web site updates, etc.). The information derived from the current toxicity data is so large and complex that it becomes difficult to process using available database management tools or traditional data processing applications. For this reason, it is necessary to develop a big data approach when conducting modern chemical toxicity research. In vitro data for a compound, obtained from meaningful bioassays, can be viewed as a response profile that gives detailed information about the compound's ability to affect relevant biological proteins/receptors. This information is critical for the evaluation of complex bioactivities (e.g., animal toxicities) and grows rapidly as big data in toxicology communities. This review focuses mainly on the existing structured in vitro data (e.g., PubChem data sets) as response profiles for compounds of environmental interest (e.g., potential human/animal toxicants). Potential modeling and mining tools to use the current big data pool in chemical toxicity research are also described.
Indexed as
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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.