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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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An analysis of entity normalization evaluation biases in specialized domains
BackgroundEntity normalization is an important information extraction task which has recently gained attention, particularly in the...
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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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B-LBConA: a medical entity disambiguation model based on Bio-LinkBERT and context-aware mechanism
BackgroundThe main task of medical entity disambiguation is to link mentions, such as diseases, drugs, or complications, to standard entities in the...
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Thrombus or tumor? A case report of a rare sarcoma entity: intimal sarcoma of the pulmonary arteries
BackgroundTumor embolism is a very rare primary manifestation of cancers and the diagnosis is challenging, especially if located in the pulmonary...
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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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Hierarchical shared transfer learning for biomedical named entity recognition
BackgroundBiomedical named entity recognition (BioNER) is a basic and important medical information extraction task to extract medical entities with...
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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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Analyzing transfer learning impact in biomedical cross-lingual named entity recognition and normalization
BackgroundThe volume of biomedical literature and clinical data is growing at an exponential rate. Therefore, efficient access to data described in...
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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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Deciphering drug discovery and microbial pathogenesis research in tuberculosis during the two decades of postgenomic era using entity mining approach
We examined literature on Mycobacterium tuberculosis (Mtb) subsequent to its genome release, spanning years 1999–2020. We employed scientometric...
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NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding
Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades
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Deep learning with language models improves named entity recognition for PharmaCoNER
BackgroundThe recognition of pharmacological substances, compounds and proteins is essential for biomedical relation extraction, knowledge graph...
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Improving the recall of biomedical named entity recognition with label re-correction and knowledge distillation
BackgroundBiomedical named entity recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from...
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Clonal haematopoiesis - a novel entity that modifies pathological processes in elderly
Progress in the development of new sequencing techniques with wider accessibility and higher sensitivity of the protocol of deciphering genome...
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Fusing Label Relations for Chinese EMR Named Entity Recognition with Machine Reading Comprehension
Chinese electronic medical records named entity recognition (NER) is a core task in medical knowledge mining, which is usually viewed as a sequence...
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Modulating the dynamics of NFκB and PI3K enhances the ensemble-level TNFR1 signaling mediated apoptotic response
Cell-to-cell variability during TNFα stimulated Tumor Necrosis Factor Receptor 1 (TNFR1) signaling can lead to single-cell level pro-survival and...
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Seroepidemiology revealed neosporosis as an under-realised entity in dairy cattle reared in South India
From the dairy herds ( n = 16) reared in few localities of South India with the history of reproductive inefficiency and incidental abortion, 176 sera...
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Chromosome-level genome assemblies of Cutaneotrichosporon spp. (Trichosporonales, Basidiomycota) reveal imbalanced evolution between nucleotide sequences and chromosome synteny
BackgroundSince DNA information was first used in taxonomy, barcode sequences such as the internal transcribed spacer (ITS) region have greatly aided...
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Cell-Level Pathway Scoring Comparison with a Biologically Constrained Variational Autoencoder
Unsupervised techniques are ubiquitous to study and understand the complex patterns that arise when analyzing genomic data at single-cell resolution....