ArticleBriefings in bioinformatics2017
Bridging semantics and syntax with graph algorithms-state-of-the-art of extracting biomedical relations.
Article in Briefings in bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 39 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
39 citing papers in PubMed, 85 citations in OpenAlex.
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- Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.Learning health systems · 2024Article
- Host-mediated gene engineering and microbiome-based technology optimization for sustainable agriculture and environment.Functional & integrative genomics · 2023Review
- Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.Scientific reports · 2023Article
- An overview of biomedical entity linking throughout the years.Journal of biomedical informatics · 2023Review
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- BertSRC: transformer-based semantic relation classification.BMC medical informatics and decision making · 2022Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels.ACM transactions on computing for healthcare · 2021Article
- A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets.BMC medical informatics and decision making · 2020Article
- The impact of learning Unified Medical Language System knowledge embeddings in relation extraction from biomedical texts.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- A corpus-driven standardization framework for encoding clinical problems with HL7 FHIR.Journal of biomedical informatics · 2020Article
- Broad-coverage biomedical relation extraction with SemRep.BMC bioinformatics · 2020Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
- Emerging Priorities for Microbiome Research.Frontiers in microbiology · 2020Review
- Extracting entities with attributes in clinical text via joint deep learning.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Towards a characterization of apparent contradictions in the biomedical literature using context analysis.Journal of biomedical informatics · 2019Article
- deepBioWSD: effective deep neural word sense disambiguation of biomedical text data.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (Seg-GCRNs).Journal of the American Medical Informatics Association : JAMIA · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
Research on extracting biomedical relations has received growing attention recently, with numerous biological and clinical applications including those in pharmacogenomics, clinical trial screening and adverse drug reaction detection. The ability to accurately capture both semantic and syntactic structures in text expressing these relations becomes increasingly critical to enable deep understanding of scientific papers and clinical narratives. Shared task challenges have been organized by both bioinformatics and clinical informatics communities to assess and advance the state-of-the-art research. Significant progress has been made in algorithm development and resource construction. In particular, graph-based approaches bridge semantics and syntax, often achieving the best performance in shared tasks. However, a number of problems at the frontiers of biomedical relation extraction continue to pose interesting challenges and present opportunities for great improvement and fruitful research. In this article, we place biomedical relation extraction against the backdrop of its versatile applications, present a gentle introduction to its general pipeline and shared resources, review the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions.
Indexed as
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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.