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Found 14 papers, 2 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.
PubMed and Beyond: Biomedical Literature Search in the Age of Artificial Intelligence
In response, we present a survey of literature search tools tailored to both general and specific information needs in biomedicine, with the objective of helping readers efficiently fulfill their information needs.
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.
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning
A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of SARS-CoV-2 infection.
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature.
The overview of the NLM-Chem BioCreative VII track: full-text chemical identification and indexing in PubMed articles
The BioCreative NLM-Chem track calls for a community effort to fine-tune automated recognition of chemical names in biomedical literature.
A Comprehensive Dictionary and Term Variation Analysis for COVID-19 and SARS-CoV-2
In this manuscript we present an extensive dictionary of terms used in the literature to refer to SARS-CoV-2 and COVID-19.
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.
Navigating the landscape of COVID-19 research through literature analysis: A bird's eye view
In this study we analyze the LitCovid collection, 13, 369 COVID-19 related articles found in PubMed as of May 15th, 2020 with the purpose of examining the landscape of literature and presenting it in a format that facilitates information navigation and understanding.
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.
Challenges in clinical natural language processing for automated disorder normalization
Conclusion Disorder mentions in text from clinical narratives use a rich vocabulary that results in high term variation, which we believe to be one of the primary causes of reduced performance in clinical narrative.
