A model for predicting drug-disease associations based on dense convolutional attention network
The development of new drugs is a time-consuming and labor-intensive process. Therefore, researchers use computational methods to explore other therapeutic effects of existing drugs, and drug-disease association prediction is an important branch of it. The existing drug-disease association predictio...
Enregistré dans:
| Auteurs principaux: | Huiqing Wang, Sen Zhao, Jing Zhao, Zhipeng Feng |
|---|---|
| Format: | article |
| Langue: | EN |
| Publié: |
AIMS Press
2021
|
| Sujets: | |
| Accès en ligne: | https://doaj.org/article/029eeab8870949b59f8b66cf9f166132 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images
par: Zijian Wang, et autres
Publié: (2021) -
Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network
par: Mariam Busaleh, et autres
Publié: (2021) -
Remote Sensing Image Scene Classification Based on Global Self-Attention Module
par: Qingwen Li, et autres
Publié: (2021) -
A Dense Encoder–Decoder Network with Feedback Connections for Pan-Sharpening
par: Weisheng Li, et autres
Publié: (2021) -
Computing the Laplace transform and the convolution for more functions adjoined
par: Sudo,Takahiro
Publié: (2015)