BioBERT: a pre-trained biomedical language representation model for biomedical text mining [PDF]
Bioinformatics, 2020 Motivation Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained ...
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim
exaly +8 more sources
SparkText: Biomedical Text Mining on Big Data Framework. [PDF]
PLoS ONE, 2016 BACKGROUND:Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments.
Zhan Ye +4 more
doaj +6 more sources
ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers [PDF]
Molecules, 2018 A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing ...
Yuting Xing +5 more
doaj +3 more sources
A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining. [PDF]
Front Genet, 2023 Syndrome differentiation and treatment is the basic principle of traditional Chinese medicine (TCM) to recognize and treat diseases. Accurate syndrome differentiation can provide a reliable basis for treatment, therefore, establishing a scientific ...
Gong L, Jiang J, Chen S, Qi M.
europepmc +2 more sources
BioVAE: a pre-trained latent variable language model for biomedical text mining. [PDF]
Bioinformatics, 2022 Summary Large-scale pre-trained language models (PLMs) have advanced state-of-the-art (SOTA) performance on various biomedical text mining tasks. The power of such PLMs can be combined with the advantages of deep generative models.
Trieu HL, Miwa M, Ananiadou S.
europepmc +2 more sources
IEEE Access, 2019 Text data plays an imperative role in the biomedical domain. As patient's data comprises of a huge amount of text documents in a non-standardized format.
Junaid Rashid +6 more
doaj +2 more sources
A Comparative Analysis of Active Learning for Biomedical Text Mining
Applied System Innovation, 2021 An enormous amount of clinical free-text information, such as pathology reports, progress reports, clinical notes and discharge summaries have been collected at hospitals and medical care clinics.
Usman Naseem +4 more
doaj +2 more sources
A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining
IEEE Access, 2019 The amount of biomedical literature is vast and growing quickly, and accurate text mining techniques could help researchers to efficiently extract useful information from the literature.
Donghyeon Kim +8 more
doaj +2 more sources
A realistic assessment of methods for extracting gene/protein interactions from free text [PDF]
BMC Bioinformatics, 2009 Background The automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research.
Shepherd Adrian J +2 more
doaj +6 more sources
FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining. [PDF]
BMC Bioinformatics, 2018 For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or “
Bachman JA, Gyori BM, Sorger PK.
europepmc +2 more sources