ArticleBMC bioinformatics2020
Broad-coverage biomedical relation extraction with SemRep.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 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
45 citing papers in PubMed.
- Automated candidate confounder scoping for adjustment in clinical research: a retrieval-augmented generation approach.BioData mining · 2026Article
- Comparison of pipelines, seq2seq models, and LLMs for rare disease information extraction.Natural language processing and information systems : ... International Conference on Applications of Natural Language to Information Systems, NLDB ... revised papers. International Conference on Applications of Natural Language to Info... · 2026Article
- AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia.Artificial intelligence in medicine · 2025Article
- Evidence triangulator: using large language models to extract and synthesize causal evidence across study designs.Nature communications · 2025Article
- DiMB-RE: mining the scientific literature for diet-microbiome associations.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Alzheimer's disease knowledge graph enhances knowledge discovery and disease prediction.Computers in biology and medicine · 2025Article
- A study on large-scale disease causality discovery from biomedical literature.BMC medical informatics and decision making · 2025Article
- Article
- Knowledge graph and its application in the study of neurological and mental disorders.Frontiers in psychiatry · 2025Review
- Triangulating evidence in health sciences with Annotated Semantic Queries.Bioinformatics (Oxford, England) · 2024Article
- On the role of the UMLS in supporting diagnosis generation proposed by Large Language Models.Journal of biomedical informatics · 2024Article
- Integrating deep learning architectures for enhanced biomedical relation extraction: a pipeline approach.Database : the journal of biological databases and curation · 2024Article
- PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.Nucleic acids research · 2024Article
- Enhancing the coverage of SemRep using a relation classification approach.Journal of biomedical informatics · 2024Article
- Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique.BMC bioinformatics · 2024Article
- Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature.Scientific reports · 2024Article
- Knowledge Graph for Breast Cancer Prevention and Treatment: Literature-Based Data Analysis Study.JMIR medical informatics · 2024Article
- Data-Driven Hypothesis Generation in Clinical Research: What We Learned from a Human Subject Study?Medical research archives · 2024Article
- Semantics-enabled biomedical literature analytics.Journal of biomedical informatics · 2024Article
- Serial KinderMiner (SKiM) discovers and annotates biomedical knowledge using co-occurrence and transformer models.BMC bioinformatics · 2023Article
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.
Funding
Abstract
backgroundIn the era of information overload, natural language processing (NLP) techniques are increasingly needed to support advanced biomedical information management and discovery applications. In this paper, we present an in-depth description of SemRep, an NLP system that extracts semantic relations from PubMed abstracts using linguistic principles and UMLS domain knowledge. We also evaluate SemRep on two datasets. In one evaluation, we use a manually annotated test collection and perform a comprehensive error analysis. In another evaluation, we assess SemRep's performance on the CDR dataset, a standard benchmark corpus annotated with causal chemical-disease relationships.
resultsA strict evaluation of SemRep on our manually annotated dataset yields 0.55 precision, 0.34 recall, and 0.42 F
conclusionsSemRep is a broad-coverage, interpretable, strong baseline system for extracting semantic relations from biomedical text. It also underpins SemMedDB, a literature-scale knowledge graph based on semantic relations. Through SemMedDB, SemRep has had significant impact in the scientific community, supporting a variety of clinical and translational applications, including clinical decision making, medical diagnosis, drug repurposing, literature-based discovery and hypothesis generation, and contributing to improved health outcomes. In ongoing development, we are redesigning SemRep to increase its modularity and flexibility, and addressing weaknesses identified in the error analysis.
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.