GNN-surv: Discrete-Time Survival Prediction Using Graph Neural Networks
Survival prediction models play a key role in patient prognosis and personalized treatment. However, their accuracy can be improved by incorporating patient similarity networks, which uncover complex data patterns. Our study uses Graph Neural Networks (GNNs) to enhance discrete-time survival predict...
Main Author: | So Yeon Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-09-01
|
Series: | Bioengineering |
Subjects: | |
Online Access: | https://www.mdpi.com/2306-5354/10/9/1046 |
Similar Items
-
LeL-GNN: Learnable Edge Sampling and Line Based Graph Neural Network for Link Prediction
by: Md Golam Morshed, et al.
Published: (2023-01-01) -
SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network
by: Xueting Han, et al.
Published: (2022-02-01) -
DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network
by: Saranya Muniyappan, et al.
Published: (2023-03-01) -
Predicting the risk of mortality in ICU patients based on dynamic graph attention network of patient similarity
by: Manfu Ma, et al.
Published: (2023-07-01) -
Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks
by: Sung Mo Ryu, et al.
Published: (2020-01-01)