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Found 11 papers, 4 papers with code
EnzChemRED, a rich enzyme chemistry relation extraction dataset
EnzChemRED consists of 1, 210 expert curated PubMed abstracts in which enzymes and the chemical reactions they catalyze are annotated using identifiers from the UniProt Knowledgebase (UniProtKB) and the ontology of Chemical Entities of Biological Interest (ChEBI).
PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge
PubTator 3. 0 (https://www. ncbi. nlm. nih. gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases, and chemicals.
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets
State-of-the-art methods were used primarily to train machine learning models on individual RE datasets, such as protein-protein interaction and chemical-induced disease relation.
Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health
In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health.
AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning
Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction and question answering.
Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework
The automatic assignment of species information to the corresponding genes in a research article is a critically important step in the gene normalization task, whereby a gene mention is normalized and linked to a database record or identifier by a text-mining algorithm.
BioRED: A Rich Biomedical Relation Extraction Dataset
However, most existing benchmarking datasets for bio-medical RE only focus on relations of a single type (e. g., protein-protein interactions) at the sentence level, greatly limiting the development of RE systems in biomedicine.
Ranked #1 on
Named Entity Recognition (NER)
on BioRED
BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer
A biomedical relation statement is commonly expressed in multiple sentences and consists of many concepts, including gene, disease, chemical, and mutation.
Ranked #2 on
Relation Extraction
on BioRED
PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology
In this paper, we propose PhenoTagger, a hybrid method that combines both dictionary and machine learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text.
Revised JNLPBA Corpus: A Revised Version of Biomedical NER Corpus for Relation Extraction Task
Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application.
Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble
In this paper, we presented a study that focuses on the DDI classification.
