Progressively Helical Multi-Omics Data Fusion GCN and Its Application in Lung Adenocarcinoma
Compared to single-omics data, utilizing multi-omics data helps to gain a more comprehensive understanding of the occurrence and development of cancer, which emphasizes the necessity of developing efficient multi-omics data fusion approaches. In this study, a novel framework based on graph convoluti...
Main Authors: | Junxuan Zhu, Jinhan Zhang, Liyan Wang, Hao Huang, Zhibo Zhang, Kai Song, Xiaofei Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10185917/ |
Similar Items
-
MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis
by: Xiao Li, et al.
Published: (2022-02-01) -
MO-GCN: A multi-omics graph convolutional network for discriminative analysis of schizophrenia
by: Haiyuan Wang, et al.
Published: (2025-02-01) -
DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data
by: Shao-Wu Zhang, et al.
Published: (2022-10-01) -
MONet: cancer driver gene identification algorithm based on integrated analysis of multi-omics data and network models
by: Yingzan Ren, et al.
Published: (2025-02-01) -
LAD-GCN: Automatic diagnostic framework for quantitative estimation of growth patterns during clinical evaluation of lung adenocarcinoma
by: Wei Xiao, et al.
Published: (2022-08-01)