ArticleBioinformatics (Oxford, England)2012
Event extraction across multiple levels of biological organization.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed.
- Biomedical event argument detection method based on multi-feature fusion and question-answer paradigm.Heliyon · 2024Article
- Effective type label-based synergistic representation learning for biomedical event trigger detection.BMC bioinformatics · 2024Article
- Surveying biomedical relation extraction: a critical examination of current datasets and the proposal of a new resource.Briefings in bioinformatics · 2024Review
- Few-shot learning for medical text: A review of advances, trends, and opportunities.Journal of biomedical informatics · 2023Review
- Child-Sum EATree-LSTMs: enhanced attentive Child-Sum Tree-LSTMs for biomedical event extraction.BMC bioinformatics · 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
- A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.NPJ digital medicine · 2022Review
- A biomedical event extraction method based on fine-grained and attention mechanism.BMC bioinformatics · 2022Article
- Conditional Probability Joint Extraction of Nested Biomedical Events: Design of a Unified Extraction Framework Based on Neural Networks.JMIR medical informatics · 2022Article
- Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature.Sensors (Basel, Switzerland) · 2021Article
- An extensive review of tools for manual annotation of documents.Briefings in bioinformatics · 2021Review
- A transfer learning model with multi-source domains for biomedical event trigger extraction.BMC genomics · 2021Article
- DeepEventMine: end-to-end neural nested event extraction from biomedical texts.Bioinformatics (Oxford, England) · 2020Article
- Biomedical event extraction with a novel combination strategy based on hybrid deep neural networks.BMC bioinformatics · 2020Article
- Multiple-level biomedical event trigger recognition with transfer learning.BMC bioinformatics · 2019Article
- Generating the Blood Exposome Database Using a Comprehensive Text Mining and Database Fusion Approach.Environmental health perspectives · 2019Article
- Bidirectional long short-term memory with CRF for detecting biomedical event trigger in FastText semantic space.BMC bioinformatics · 2018Article
- Annotation and detection of drug effects in text for pharmacovigilance.Journal of cheminformatics · 2018Article
- Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed.Nature biotechnology · 2018Article
- Exploiting and assessing multi-source data for supervised biomedical named entity recognition.Bioinformatics (Oxford, England) · 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
6 authors.
Funding
Abstract
motivationEvent extraction using expressive structured representations has been a significant focus of recent efforts in biomedical information extraction. However, event extraction resources and methods have so far focused almost exclusively on molecular-level entities and processes, limiting their applicability.
resultsWe extend the event extraction approach to biomedical information extraction to encompass all levels of biological organization from the molecular to the whole organism. We present the ontological foundations, target types and guidelines for entity and event annotation and introduce the new multi-level event extraction (MLEE) corpus, manually annotated using a structured representation for event extraction. We further adapt and evaluate named entity and event extraction methods for the new task, demonstrating that both can be achieved with performance broadly comparable with that for established molecular entity and event extraction tasks. AVAILABILITY: The resources and methods introduced in this study are available from http://nactem.ac.uk/MLEE/. CONTACT: pyysalos@cs.man.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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.