ArticleEnvironment international2021
Prioritizing cancer hazard assessments for IARC Monographs using an integrated approach of database fusion and text mining.
Article in Environment international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Simultaneous Determination of Three Residual Monomers in Polyvinyl Alcohol Eye Drops Using a Dual-Wavelength RP-HPLC Method and Evaluation of Its Greenness.Biomedical chromatography : BMC · 2026Article
- Predicting toxicity and bioactivity of the chemical exposome: a case study for the blood exposome database.Journal of cheminformatics · 2026Article
- CarD-T: an automated pipeline for the nomination and analysis of potential human carcinogens.Carcinogenesis · 2025Article
- Predicting Toxicity and Bioactivity of the Chemical Exposome: A Case Study for the Blood Exposome Database.bioRxiv : the preprint server for biology · 2025Article
- IARC Workshop on the Key Characteristics of Carcinogens: Assessment of End Points for Evaluating Mechanistic Evidence of Carcinogenic Hazards.Environmental health perspectives · 2025Article
- Molecular and cell phenotype programs in oral epithelial cells directed by co-exposure to arsenic and smokeless tobacco.bioRxiv : the preprint server for biology · 2024Article
- CarD-T: Interpreting Carcinomic Lexicon via Transformers.medRxiv : the preprint server for health sciences · 2024Article
- Advisory Group recommendations on priorities for the IARC Monographs.The Lancet. Oncology · 2024Article
- Research Recommendations for Selected IARC-Classified Agents: Impact and Lessons Learned.Environmental health perspectives · 2023Review
- A Narrative Literature Review of Natural Language Processing Applied to the Occupational Exposome.International journal of environmental research and public health · 2022Review
- CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets.Environment international · 2022Article
- Automated Network Assembly of Mechanistic Literature for Informed Evidence Identification to Support Cancer Risk Assessment.Environmental health perspectives · 2022Article
- Molecular and cell phenotype programs in oral epithelial cells directed by co-exposure to arsenic and smokeless tobacco.BioFactors (Oxford, England)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundSystematic evaluation of literature data on the cancer hazards of human exposures is an essential process underlying cancer prevention strategies. The scope and volume of evidence for suspected carcinogens can range from very few to thousands of publications, requiring a complex, systematically planned, and critical procedure to nominate, prioritize and evaluate carcinogenic agents. To aid in this process, database fusion, cheminformatics and text mining techniques can be combined into an integrated approach to inform agent prioritization, selection, and grouping.
resultsWe have applied these techniques to agents recommended for the IARC Monographs evaluations during 2020-2024. An integration of PubMed filters to cover cancer epidemiology, key characteristics of carcinogens, chemical lists from 34 databases relevant for cancer research, chemical structure grouping and a literature data-based clustering was applied in an innovative approach to 119 agents recommended by an advisory group for future IARC Monographs evaluations. The approach also facilitated a rational grouping of these agents and aids in understanding the volume and complexity of relevant information, as well as important gaps in coverage of the available studies on cancer etiology and carcinogenesis.
conclusionA new data-science approach has been applied to diverse agents recommended for cancer hazard assessments, and its applications for the IARC Monographs are demonstrated. The prioritization approach has been made available at www.cancer.idsl.me site for ranking cancer agents.
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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.