MAVGAE: a multimodal framework for predicting asymmetric drug–drug interactions based on variational graph autoencoder-Reference-Cited by-同舟云学术

MAVGAE: a multimodal framework for predicting asymmetric drug–drug interactions based on variational graph autoencoder

Published:2024-02-05 Issue: Volume: Page:1-13
ISSN:1025-5842
Container-title:Computer Methods in Biomechanics and Biomedical Engineering
language:en
Short-container-title:Computer Methods in Biomechanics and Biomedical Engineering

Author:

Deng Zengqian¹,Xu Jie²,Feng Yinfei¹,Dong Liangcheng¹,Zhang Yuanyuan¹

Affiliation:

1. School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China

2. School of Information Science and Engineering, Qufu Normal University, Rizhao, China

Funder

Natural Science Foundation of Shandong Province

the National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10255842.2024.2311315

Reference35 articles.

1. Abstract 4100: Mechanisms of resistance to type I and type II MET inhibitors in non-small cell lung cancer

2. High potency of sequential therapy with only β-lactam antibiotics

3. Chen L, Lin S-Y, Xie Y, Lin Y-Y, Fan W, Xie X. 2020. DGGAN: depth-image guided generative adversarial networks for disentangling RGB and depth images in 3D hand pose estimation. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, p. 411–419.

4. Sequential Therapy of Acute Kidney Injury with a DNA Nanodevice

5. A multimodal deep learning framework for predicting drug–drug interaction events

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