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JCBIE: a joint continual learning neural network for biomedical information extraction
Extracting knowledge from heterogeneous data sources is fundamental for the construction of structured biomedical knowledge graphs (BKGs), where...
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A biomedical event extraction method based on fine-grained and attention mechanism
BackgroundBiomedical event extraction is a fundamental task in biomedical text mining, which provides inspiration for medicine research and disease...
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Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts
BackgroundKnowledge graphs support biomedical research efforts by providing contextual information for biomedical entities, constructing networks,...
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BioEGRE: a linguistic topology enhanced method for biomedical relation extraction based on BioELECTRA and graph pointer neural network
BackgroundAutomatic and accurate extraction of diverse biomedical relations from literature is a crucial component of bio-medical text mining....
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Biomedical relation extraction via knowledge-enhanced reading comprehension
BackgroundIn biomedical research, chemical and disease relation extraction from unstructured biomedical literature is an essential task. Effective...
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A Combined Manual Annotation and Deep-Learning Natural Language Processing Study on Accurate Entity Extraction in Hereditary Disease Related Biomedical Literature
We report a combined manual annotation and deep-learning natural language processing study to make accurate entity extraction in hereditary disease...
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TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction
BackgroundDatabases are fundamental to advance biomedical science. However, most of them are populated and updated with a great deal of human effort....
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IMSE: interaction information attention and molecular structure based drug drug interaction extraction
BackgroundExtraction of drug drug interactions from biomedical literature and other textual data is an important component to monitor drug-safety and...
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Investigation of improving the pre-training and fine-tuning of BERT model for biomedical relation extraction
BackgroundRecently, automatically extracting biomedical relations has been a significant subject in biomedical research due to the rapid growth of...
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Child-Sum EATree-LSTMs: enhanced attentive Child-Sum Tree-LSTMs for biomedical event extraction
BackgroundTree-structured neural networks have shown promise in extracting lexical representations of sentence syntactic structures, particularly in...
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Biomedical semantic text summarizer
BackgroundText summarization is a challenging problem in Natural Language Processing, which involves condensing the content of textual documents...
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Improving deep learning method for biomedical named entity recognition by using entity definition information
BackgroundBiomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in...
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Digital surveillance in Latin American diseases outbreaks: information extraction from a novel Spanish corpus
BackgroundIn order to detect threats to public health and to be well-prepared for endemic and pandemic illness outbreaks, countries usually rely on...
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Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique
BackgroundAutomated hypothesis generation (HG) focuses on uncovering hidden connections within the extensive information that is publicly available....
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MetaTron: advancing biomedical annotation empowering relation annotation and collaboration
BackgroundThe constant growth of biomedical data is accompanied by the need for new methodologies to effectively and efficiently extract...
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Biomedical Literature Mining and Its Components
The published biomedical articles are the best source of knowledge to understand the importance of biomedical entities such as disease, drugs, and...
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A pipeline for the retrieval and extraction of domain-specific information with application to COVID-19 immune signatures
BackgroundThe accelerating pace of biomedical publication has made it impractical to manually, systematically identify papers containing specific...
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Comparisons of Knowledge Graphs and Entity Extraction in Breast Cancer Subty** Biomedical Text Analysis
In order to capitalize on the extensive biological research publications and databases, knowledge graphs can help extract clinically useful details...
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A prefix and attention map discrimination fusion guided attention for biomedical named entity recognition
BackgroundThe biomedical literature is growing rapidly, and it is increasingly important to extract meaningful information from the vast amount of...
