Search Results for author:
Found 13 papers, 6 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.
Bioformer: an efficient transformer language model for biomedical text mining
We thoroughly evaluated the performance of Bioformer as well as existing biomedical BERT models including BioBERT and PubMedBERT on 15 benchmark datasets of four different biomedical NLP tasks: named entity recognition, relation extraction, question answering and document classification.
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.
LitCovid in 2022: an information resource for the COVID-19 literature
LitCovid (https://www. ncbi. nlm. nih. gov/research/coronavirus/), first launched in February 2020, is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19.
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
tmVar 3.0: an improved variant concept recognition and normalization tool
We have also processed the entire PubMed and PMC with tmVar3 and released its annotations on our FTP.
Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)
The COVID-19 pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread.
BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale
Capturing the semantics of related biological concepts, such as genes and mutations, is of significant importance to many research tasks in computational biology such as protein-protein interaction detection, gene-drug association prediction, and biomedical literature-based discovery.
Biomedical Mention Disambiguation using a Deep Learning Approach
The priority ordering rule-based approach demonstrated F1-scores of 71. 29% (micro-averaged) and 41. 19% (macro-averaged), while the new disambiguation method demonstrated F1-scores of 91. 94% (micro-averaged) and 85. 42% (macro-averaged), a very substantial increase.
BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations
The Precision Medicine Track in BioCre-ative VI aims to bring together the Bi-oNLP community for a novel challenge focused on mining the biomedical litera-ture in search of mutations and protein-protein interactions (PPI).
