Cargando…
Drug-Drug Interaction Extraction via Recurrent Hybrid Convolutional Neural Networks with an Improved Focal Loss
Drug-drug interactions (DDIs) may bring huge health risks and dangerous effects to a patient’s body when taking two or more drugs at the same time or within a certain period of time. Therefore, the automatic extraction of unknown DDIs has great potential for the development of pharmaceutical agents...
Autores principales: | Sun, Xia, Dong, Ke, Ma, Long, Sutcliffe, Richard, He, Feijuan, Chen, Sushing, Feng, Jun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514143/ https://www.ncbi.nlm.nih.gov/pubmed/33266753 http://dx.doi.org/10.3390/e21010037 |
Ejemplares similares
-
Drug-Drug Interaction Extraction via Convolutional Neural Networks
por: Liu, Shengyu, et al.
Publicado: (2016) -
Exploring convolutional neural networks for drug–drug interaction extraction
por: Suárez-Paniagua, Víctor, et al.
Publicado: (2017) -
A hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss
por: Wang, Tao, et al.
Publicado: (2020) -
Drug drug interaction extraction from biomedical literature using syntax convolutional neural network
por: Zhao, Zhehuan, et al.
Publicado: (2016) -
Evaluation of pooling operations in convolutional architectures for drug-drug interaction extraction
por: Suárez-Paniagua, Víctor, et al.
Publicado: (2018)