ArticleBMC bioinformatics2009
Text mining and manual curation of chemical-gene-disease networks for the comparative toxicogenomics database (CTD).
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 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
68 citing papers in PubMed.
- Article
- Integrating AI-powered text mining from PubTator into the manual curation workflow at the Comparative Toxicogenomics Database.Database : the journal of biological databases and curation · 2025Article
- Helping authors produce FAIR taxonomic data: evaluation of an author-driven phenotype data production prototype.Database : the journal of biological databases and curation · 2025Article
- Evaluation of a text-mining application for the rapid analysis of free-text wildlife necropsy reports.PloS one · 2025Article
- Exploring Toxicity of Per- and Polyfluoroalkyl Substances (PFAS) Mixture Through ADMET and Toxicogenomic In Silico Analysis: Molecular Insights.International journal of molecular sciences · 2024Article
- Article
- Joint impact of key air pollutants on COVID-19 severity: prediction based on toxicogenomic data analysis.Arhiv za higijenu rada i toksikologiju · 2022Article
- A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Learning from low-rank multimodal representations for predicting disease-drug associations.BMC medical informatics and decision making · 2021Article
- Comparative Toxicogenomics Database (CTD): update 2021.Nucleic acids research · 2021Article
- Measurement Recorder: developing a useful tool for making species descriptions that produces computable phenotypes.Database : the journal of biological databases and curation · 2020Article
- Chemical-induced disease relation extraction via attention-based distant supervision.BMC bioinformatics · 2019Article
- Automated assessment of biological database assertions using the scientific literature.BMC bioinformatics · 2019Article
- Gene Saturation: An Approach to Assess Exploration Stage of Gene Interaction Networks.Scientific reports · 2019Article
- The Comparative Toxicogenomics Database: update 2019.Nucleic acids research · 2019Article
- Identification of conclusive association entities in biomedical articles.Journal of biomedical semantics · 2019Article
- Article
- Predicting drug-disease associations by using similarity constrained matrix factorization.BMC bioinformatics · 2018Article
- Analysis of expression profile data identifies key genes and pathways in hepatocellular carcinoma.Oncology letters · 2018Article
- Overview of the BioCreative VI text-mining services for Kinome Curation Track.Database : the journal of biological databases and curation · 2018Article
8 more citing papers are in PubMed but not listed here.
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
backgroundThe Comparative Toxicogenomics Database (CTD) is a publicly available resource that promotes understanding about the etiology of environmental diseases. It provides manually curated chemical-gene/protein interactions and chemical- and gene-disease relationships from the peer-reviewed, published literature. The goals of the research reported here were to establish a baseline analysis of current CTD curation, develop a text-mining prototype from readily available open source components, and evaluate its potential value in augmenting curation efficiency and increasing data coverage.
resultsPrototype text-mining applications were developed and evaluated using a CTD data set consisting of manually curated molecular interactions and relationships from 1,600 documents. Preliminary results indicated that the prototype found 80% of the gene, chemical, and disease terms appearing in curated interactions. These terms were used to re-rank documents for curation, resulting in increases in mean average precision (63% for the baseline vs. 73% for a rule-based re-ranking), and in the correlation coefficient of rank vs. number of curatable interactions per document (baseline 0.14 vs. 0.38 for the rule-based re-ranking).
conclusionThis text-mining project is unique in its integration of existing tools into a single workflow with direct application to CTD. We performed a baseline assessment of the inter-curator consistency and coverage in CTD, which allowed us to measure the potential of these integrated tools to improve prioritization of journal articles for manual curation. Our study presents a feasible and cost-effective approach for developing a text mining solution to enhance manual curation throughput and efficiency.
Indexed as
Identifiers
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.