Cargando…
BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale
A massive number of biological entities, such as genes and mutations, are mentioned in the biomedical literature. The capturing of the semantic relatedness of biological entities is vital to many biological applications, such as protein-protein interaction prediction and literature-based discovery....
Autores principales: | Chen, Qingyu, Lee, Kyubum, Yan, Shankai, Kim, Sun, Wei, Chih-Hsuan, Lu, Zhiyong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237030/ https://www.ncbi.nlm.nih.gov/pubmed/32324731 http://dx.doi.org/10.1371/journal.pcbi.1007617 |
Ejemplares similares
-
BioWordVec, improving biomedical word embeddings with subword information and MeSH
por: Zhang, Yijia, et al.
Publicado: (2019) -
Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature
por: Lee, Kyubum, et al.
Publicado: (2020) -
ezTag: tagging biomedical concepts via interactive learning
por: Kwon, Dongseop, et al.
Publicado: (2018) -
Quality of word and concept embeddings in targetted biomedical domains
por: Giancani, Salvatore, et al.
Publicado: (2023) -
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets
por: Lai, Po-Ting, et al.
Publicado: (2023)