ArticleBMC bioinformatics2015
Overview of the Cancer Genetics and Pathway Curation tasks of BioNLP Shared Task 2013.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed.
- DiMB-RE: mining the scientific literature for diet-microbiome associations.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Bioregulatory event extraction using large language models: a case study of rice literature.Genomics & informatics · 2024Article
- A natural language processing system for the efficient extraction of cell markers.Scientific reports · 2024Article
- Biomedical event argument detection method based on multi-feature fusion and question-answer paradigm.Heliyon · 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
- Effective type label-based synergistic representation learning for biomedical event trigger detection.BMC bioinformatics · 2024Article
- What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2023Article
- A novel corpus of molecular to higher-order events that facilitates the understanding of the pathogenic mechanisms of idiopathic pulmonary fibrosis.Scientific reports · 2023Article
- Comparing neural models for nested and overlapping biomedical event detection.BMC bioinformatics · 2022Article
- Question-aware transformer models for consumer health question summarization.Journal of biomedical informatics · 2022Article
- NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories.Frontiers in physiology · 2022Article
- Medical Health Records-Based Mild Cognitive Impairment (MCI) Prediction for Effective Dementia Care.International journal of environmental research and public health · 2021Article
- OryzaGP 2021 update: a rice gene and protein dataset for named-entity recognition.Genomics & informatics · 2021Article
- A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation.Journal of medical Internet research · 2021Article
- ECO-CollecTF: A Corpus of Annotated Evidence-Based Assertions in Biomedical Manuscripts.Frontiers in research metrics and analytics · 2021Article
- Article
- DeepEventMine: end-to-end neural nested event extraction from biomedical texts.Bioinformatics (Oxford, England) · 2020Article
- Discovery of disease- and drug-specific pathways through community structures of a literature network.Bioinformatics (Oxford, England) · 2020Article
- Biomedical event extraction with a novel combination strategy based on hybrid deep neural networks.BMC bioinformatics · 2020Article
- Augmenting subnetwork inference with information extracted from the scientific literature.PLoS computational biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundSince their introduction in 2009, the BioNLP Shared Task events have been instrumental in advancing the development of methods and resources for the automatic extraction of information from the biomedical literature. In this paper, we present the Cancer Genetics (CG) and Pathway Curation (PC) tasks, two event extraction tasks introduced in the BioNLP Shared Task 2013. The CG task focuses on cancer, emphasizing the extraction of physiological and pathological processes at various levels of biological organization, and the PC task targets reactions relevant to the development of biomolecular pathway models, defining its extraction targets on the basis of established pathway representations and ontologies.
resultsSix groups participated in the CG task and two groups in the PC task, together applying a wide range of extraction approaches including both established state-of-the-art systems and newly introduced extraction methods. The best-performing systems achieved F-scores of 55% on the CG task and 53% on the PC task, demonstrating a level of performance comparable to the best results achieved in similar previously proposed tasks.
conclusionsThe results indicate that existing event extraction technology can generalize to meet the novel challenges represented by the CG and PC task settings, suggesting that extraction methods are capable of supporting the construction of knowledge bases on the molecular mechanisms of cancer and the curation of biomolecular pathway models. The CG and PC tasks continue as open challenges for all interested parties, with data, tools and resources available from the shared task homepage.
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.