ArticleBioinformatics (Oxford, England)2016
DTMiner: identification of potential disease targets through biomedical literature mining.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- SigMine and OPathDb: a literature-mining pipeline and database of potential opportunistic pathogens.Archives of microbiology · 2026Article
- CORACLE (COVID-19 liteRAture CompiLEr): A platform for efficient tracking and extraction of SARS-CoV-2 and COVID-19 literature, with examples from post-COVID with respiratory involvement.Computational and structural biotechnology journal · 2024Article
- A large language model framework for literature-based disease-gene association prediction.Briefings in bioinformatics · 2024Article
- Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.BioData mining · 2022Article
- RENET2: high-performance full-text gene-disease relation extraction with iterative training data expansion.NAR genomics and bioinformatics · 2021Article
- Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model.Scientific reports · 2021Article
- Oral lichen planus interactome reveals CXCR4 and CXCL12 as candidate therapeutic targets.Scientific reports · 2020Article
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
- Article
- Exploiting and assessing multi-source data for supervised biomedical named entity recognition.Bioinformatics (Oxford, England) · 2018Article
- Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature.Journal of medical Internet research · 2018Article
- ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.Molecules (Basel, Switzerland) · 2018Article
- Automatic extraction of gene-disease associations from literature using joint ensemble learning.PloS one · 2018Article
- DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants.Nucleic acids research · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationBiomedical researchers often search through massive catalogues of literature to look for potential relationships between genes and diseases. Given the rapid growth of biomedical literature, automatic relation extraction, a crucial technology in biomedical literature mining, has shown great potential to support research of gene-related diseases. Existing work in this field has produced datasets that are limited both in scale and accuracy.
resultsIn this study, we propose a reliable and efficient framework that takes large biomedical literature repositories as inputs, identifies credible relationships between diseases and genes, and presents possible genes related to a given disease and possible diseases related to a given gene. The framework incorporates name entity recognition (NER), which identifies occurrences of genes and diseases in texts, association detection whereby we extract and evaluate features from gene-disease pairs, and ranking algorithms that estimate how closely the pairs are related. The F1-score of the NER phase is 0.87, which is higher than existing studies. The association detection phase takes drastically less time than previous work while maintaining a comparable F1-score of 0.86. The end-to-end result achieves a 0.259 F1-score for the top 50 genes associated with a disease, which performs better than previous work. In addition, we released a web service for public use of the dataset. AVAILABILITY AND IMPLEMENTATION: The implementation of the proposed algorithms is publicly available at http://gdr-web.rwebox.com/public_html/index.php?page=download.php The web service is available at http://gdr-web.rwebox.com/public_html/index.php CONTACT: jenny.wei@astrazeneca.com or kzhu@cs.sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
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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.