ArticleBioinformatics (Oxford, England)2013
tmVar: a text mining approach for extracting sequence variants in biomedical literature.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers.
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Who cites it
95 citing papers in PubMed.
- Trial
- PubMind: literature-based genetic variant extraction and functional annotation using large language models.Nature communications · 2026Article
- What Do Biomedical NER and Entity Linking Benchmarks Measure? A Corpus-Centric Diagnostic Framework.ArXiv · 2026Article
- Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The biomedical relationship corpus of the BioRED track at the BioCreative VIII challenge and workshop.Database : the journal of biological databases and curation · 2024Article
- Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction.European journal of medical research · 2024Article
- Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition.Journal of proteome research · 2024Article
- PubMed and beyond: biomedical literature search in the age of artificial intelligence.EBioMedicine · 2024Review
- Artificial Intelligence-assisted Biomedical Literature Knowledge Synthesis to Support Decision-making in Precision Oncology.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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- Named Entity Recognition and Normalization for Alzheimer's Disease Eligibility Criteria.Proceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- ViMRT: a text-mining tool and search engine for automated virus mutation recognition.Bioinformatics (Oxford, England) · 2023Article
- BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework.BMC bioinformatics · 2022Article
- PlagueKD: a knowledge graph-based plague knowledge database.Database : the journal of biological databases and curation · 2022Article
- tmVar 3.0: an improved variant concept recognition and normalization tool.Bioinformatics (Oxford, England) · 2022Article
- Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Article
- Accelerated variant curation from scientific literature using biomedical text mining.microPublication biology · 2022Article
- DES-Tcell is a knowledgebase for exploring immunology-related literature.Scientific reports · 2021Article
- NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition.Journal of biomedical informatics · 2021Article
35 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
motivationText-mining mutation information from the literature becomes a critical part of the bioinformatics approach for the analysis and interpretation of sequence variations in complex diseases in the post-genomic era. It has also been used for assisting the creation of disease-related mutation databases. Most of existing approaches are rule-based and focus on limited types of sequence variations, such as protein point mutations. Thus, extending their extraction scope requires significant manual efforts in examining new instances and developing corresponding rules. As such, new automatic approaches are greatly needed for extracting different kinds of mutations with high accuracy.
resultsHere, we report tmVar, a text-mining approach based on conditional random field (CRF) for extracting a wide range of sequence variants described at protein, DNA and RNA levels according to a standard nomenclature developed by the Human Genome Variation Society. By doing so, we cover several important types of mutations that were not considered in past studies. Using a novel CRF label model and feature set, our method achieves higher performance than a state-of-the-art method on both our corpus (91.4 versus 78.1% in F-measure) and their own gold standard (93.9 versus 89.4% in F-measure). These results suggest that tmVar is a high-performance method for mutation extraction from biomedical literature. AVAILABILITY: tmVar software and its corpus of 500 manually curated abstracts are available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/pub/tmVar
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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.