ReviewBioMed research international2014
A knowledge-driven approach to extract disease-related biomarkers from the literature.
Review in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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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
30 citing papers in PubMed.
- Integrative analysis identifies a glycosylation-related lncRNA signature associated with prognosis in kidney renal clear cell carcinoma.Translational andrology and urology · 2026Article
- ExIR enables prioritizing driver and biomarker genes from omics data in a reference free manner.iScience · 2026Article
- Ontology-driven association rule mining for biomedical entity relationships: integrating hierarchical knowledge to improve gene-disease discovery.Scientific reports · 2026Article
- AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer's disease using high-throughput sequencing data.Scientific reports · 2024Article
- XGDAG: explainable gene-disease associations via graph neural networks.Bioinformatics (Oxford, England) · 2023Article
- NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification.Bioinformatics (Oxford, England) · 2023Article
- Salivary protein candidates for biomarkers of oral disorders in people with a crack cocaine use disorder.Journal of applied oral science : revista FOB · 2023Article
- Identifying and Validating Networks of Oncology Biomarkers Mined From the Scientific Literature.Cancer informatics · 2022Article
- COVIDium: a COVID-19 resource compendium.Database : the journal of biological databases and curation · 2021Article
- Article
- BIONDA: a free database for a fast information on published biomarkers.Bioinformatics advances · 2021Article
- DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases.Oxidative medicine and cellular longevity · 2020Article
- Identification of infectious disease-associated host genes using machine learning techniques.BMC bioinformatics · 2019Article
- OGER++: hybrid multi-type entity recognition.Journal of cheminformatics · 2019Article
- Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson.PloS one · 2019Article
- Literature-Based Enrichment Insights into Redox Control of Vascular Biology.Oxidative medicine and cellular longevity · 2019Review
- Automated extraction of Biomarker information from pathology reports.BMC medical informatics and decision making · 2018Article
- ILDgenDB: integrated genetic knowledge resource for interstitial lung diseases (ILDs).Database : the journal of biological databases and curation · 2018Article
- Automatic extraction of gene-disease associations from literature using joint ensemble learning.PloS one · 2018Article
- Text Mining in Biomedical Domain with Emphasis on Document Clustering.Healthcare informatics research · 2017Review
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
No grant is acknowledged in the PubMed record.
Abstract
The biomedical literature represents a rich source of biomarker information. However, both the size of literature databases and their lack of standardization hamper the automatic exploitation of the information contained in these resources. Text mining approaches have proven to be useful for the exploitation of information contained in the scientific publications. Here, we show that a knowledge-driven text mining approach can exploit a large literature database to extract a dataset of biomarkers related to diseases covering all therapeutic areas. Our methodology takes advantage of the annotation of MEDLINE publications pertaining to biomarkers with MeSH terms, narrowing the search to specific publications and, therefore, minimizing the false positive ratio. It is based on a dictionary-based named entity recognition system and a relation extraction module. The application of this methodology resulted in the identification of 131,012 disease-biomarker associations between 2,803 genes and 2,751 diseases, and represents a valuable knowledge base for those interested in disease-related biomarkers. Additionally, we present a bibliometric analysis of the journals reporting biomarker related information during the last 40 years.
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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.