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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...
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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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Biomedical named entity recognition with the combined feature attention and fully-shared multi-task learning
BackgroundBiomedical named entity recognition (BioNER) is a basic and important task for biomedical text mining with the purpose of automatically...
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BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework
BackgroundAutomatic and accurate recognition of various biomedical named entities from literature is an important task of biomedical text mining,...
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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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Extract antibody and antigen names from biomedical literature
BackgroundThe roles of antibody and antigen are indispensable in targeted diagnosis, therapy, and biomedical discovery. On top of that, massive...
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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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Benchmarking for biomedical natural language processing tasks with a domain specific ALBERT
BackgroundThe abundance of biomedical text data coupled with advances in natural language processing (NLP) is resulting in novel biomedical NLP...
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Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts
Background and objectiveAlthough rare diseases are characterized by low prevalence, approximately 400 million people are affected by a rare disease....
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Predicting drug characteristics using biomedical text embedding
BackgroundDrug–drug interactions (DDIs) are preventable causes of medical injuries and often result in doctor and emergency room visits. Previous...
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Relation Predictions in Comorbid Disease Centric Knowledge Graph Using Heterogeneous GNN Models
Disease comorbidity has been an important topic of research for the last decade. This topic has become more popular due to the recent outbreak of...
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Gaussian-Enhanced Representation Model for Extracting Protein-Protein Interactions Affected by Mutations
The biomedical literature contains many protein-protein interactions (PPIs) affected by genetic mutations. Automatic extraction of PPIs affected by...
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Parallel sequence tagging for concept recognition
BackgroundNamed Entity Recognition (NER) and Normalisation (NEN) are core components of any text-mining system for biomedical texts. In a traditional...
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Concept recognition as a machine translation problem
BackgroundAutomated assignment of specific ontology concepts to mentions in text is a critical task in biomedical natural language processing, and...
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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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Refining electronic medical records representation in manifold subspace
BackgroundElectronic medical records (EMR) contain detailed information about patient health. Develo** an effective representation model is of...
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External features enriched model for biomedical question answering
BackgroundBiomedical question answering (QA) is a sub-task of natural language processing in a specific domain, which aims to answer a question in...
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Context-aware multi-token concept recognition of biological entities
BackgroundConcept recognition is a term that corresponds to the two sequential steps of named entity recognition and named entity normalization, and...
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Application of Supervised Machine Learning to Extract Brain Connectivity Information from Neuroscience Research Articles
AbstractUnderstanding the complex connectivity structure of the brain is a major challenge in neuroscience. Vast and ever-expanding literature about...
