Risk stratification and pathway analysis based on graph neural network and interpretable algorithm
Abstract Background Pathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the...
Main Authors: | Bilin Liang, Haifan Gong, Lu Lu, Jie Xu |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2022-09-01
|
Series: | BMC Bioinformatics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12859-022-04950-1 |
Similar Items
-
Interpretable neural networks: principles and applications
by: Zhuoyang Liu, et al.
Published: (2023-10-01) -
An End-to-End Multiplex Graph Neural Network for Graph Representation Learning
by: Yanyan Liang, et al.
Published: (2021-01-01) -
Multi-task transient stability assessment of power system based on graph neural network with interpretable attribution analysis
by: Sili Gu, et al.
Published: (2023-10-01) -
Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling
by: Zehao Dong, et al.
Published: (2023-08-01) -
Explicit Feature Interaction-Aware Graph Neural Network
by: Minkyu Kim, et al.
Published: (2024-01-01)