ArticlePloS one2016
DiMeX: A Text Mining System for Mutation-Disease Association Extraction.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 67 citations in OpenAlex.
- Integrating text mining and knowledge graph to enhance biopharmaceutical process optimization.PloS one · 2026Article
- SciLinker: a large-scale text mining framework for mapping associations among biological entities.Frontiers in artificial intelligence · 2025Article
- BioGSF: a graph-driven semantic feature integration framework for biomedical relation extraction.Briefings in bioinformatics · 2024Article
- RelCurator: a text mining-based curation system for extracting gene-phenotype relationships specific to neurodegenerative disorders.Genes & genomics · 2023Article
- Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method.BMC bioinformatics · 2023Article
- Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes.BMC bioinformatics · 2021Article
- ResidueFinder: extracting individual residue mentions from protein literature.Journal of biomedical semantics · 2021Article
- Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature.Briefings in bioinformatics · 2021Review
- Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices.JCO clinical cancer informatics · 2020Review
- OncoMX: A Knowledgebase for Exploring Cancer Biomarkers in the Context of Related Cancer and Healthy Data.JCO clinical cancer informatics · 2020Article
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
- PGxMine: Text mining for curation of PharmGKB.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
- Article
- PubTator central: automated concept annotation for biomedical full text articles.Nucleic acids research · 2019Article
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.Human genetics · 2019Review
- Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine.Database : the journal of biological databases and curation · 2019Article
- DES-Mutation: System for Exploring Links of Mutations and Diseases.Scientific reports · 2018Article
- Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed.Nature biotechnology · 2018Article
- Loss and gain of N-linked glycosylation sequons due to single-nucleotide variation in cancer.Scientific reports · 2018Article
- Deep learning of mutation-gene-drug relations from the literature.BMC bioinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases.
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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.