ArticlePloS one2012
Text mining for literature review and knowledge discovery in cancer risk assessment and research.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 77 citations in OpenAlex.
- Gender differences in cancer susceptibility: role of oxidative stress.Carcinogenesis · 2016Pooled it
- Text mining applications in psychiatry: a systematic literature review.International journal of methods in psychiatric research · 2016Pooled it
- Toward the automation of family semantic grids: a pilot exploration of text mining for evidence-informed systemic-relational psychotherapy.Frontiers in child and adolescent psychiatry · 2026Article
- IARC Workshop on the Key Characteristics of Carcinogens: Assessment of End Points for Evaluating Mechanistic Evidence of Carcinogenic Hazards.Environmental health perspectives · 2025Article
- Cancer-related Keywords in 2023: Insights from Text Mining of a Major Consumer Portal.Healthcare informatics research · 2024Article
- Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.PloS one · 2024Article
- A Narrative Literature Review of Natural Language Processing Applied to the Occupational Exposome.International journal of environmental research and public health · 2022Review
- Text Mining for Building Biomedical Networks Using Cancer as a Case Study.Biomolecules · 2021Review
- Application of Text Mining in Risk Assessment of Chemical Mixtures: A Case Study of Polycyclic Aromatic Hydrocarbons (PAHs).Environmental health perspectives · 2021Article
- A text mining-based thematic model for analyzing construction and demolition waste management studies.Environmental science and pollution research international · 2021Review
- Leveraging Social Media Activity and Machine Learning for HIV and Substance Abuse Risk Assessment: Development and Validation Study.Journal of medical Internet research · 2021Article
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- MicroRNA-135a-5p Promotes the Functional Recovery of Spinal Cord Injury by Targeting SP1 and ROCK.Molecular therapy. Nucleic acids · 2020Article
- Artificial Intelligence for chemical risk assessment.Computational toxicology (Amsterdam, Netherlands) · 2020Article
- DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases.Oxidative medicine and cellular longevity · 2020Article
- ProtFus: A Comprehensive Method Characterizing Protein-Protein Interactions of Fusion Proteins.PLoS computational biology · 2019Article
- Literature-Based Enrichment Insights into Redox Control of Vascular Biology.Oxidative medicine and cellular longevity · 2019Review
- MergedTrie: Efficient textual indexing.PloS one · 2019Article
- Global Genetics Research in Prostate Cancer: A Text Mining and Computational Network Theory Approach.Frontiers in genetics · 2019Article
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 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research in biomedical text mining is starting to produce technology which can make information in biomedical literature more accessible for bio-scientists. One of the current challenges is to integrate and refine this technology to support real-life scientific tasks in biomedicine, and to evaluate its usefulness in the context of such tasks. We describe CRAB - a fully integrated text mining tool designed to support chemical health risk assessment. This task is complex and time-consuming, requiring a thorough review of existing scientific data on a particular chemical. Covering human, animal, cellular and other mechanistic data from various fields of biomedicine, this is highly varied and therefore difficult to harvest from literature databases via manual means. Our tool automates the process by extracting relevant scientific data in published literature and classifying it according to multiple qualitative dimensions. Developed in close collaboration with risk assessors, the tool allows navigating the classified dataset in various ways and sharing the data with other users. We present a direct and user-based evaluation which shows that the technology integrated in the tool is highly accurate, and report a number of case studies which demonstrate how the tool can be used to support scientific discovery in cancer risk assessment and research. Our work demonstrates the usefulness of a text mining pipeline in facilitating complex research tasks in biomedicine. We discuss further development and application of our technology to other types of chemical risk assessment in the future.
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.