LASSO–MOGAT: a multi-omics graph attention framework for cancer classification
The application of machine learning (ML) methods to analyze changes in gene expression patterns has recently emerged as a powerful approach in cancer research, enhancing our understanding of the molecular mechanisms underpinning cancer development and progression. Combining gene expressio...
Saved in:
Main Authors: | Fadi Alharbi, Aleksandar Vakanski, Murtada K. Elbashir, Mohanad Mohammed |
---|---|
Format: | Article |
Language: | English |
Published: |
Academia.edu Journals
2024-08-01
|
Series: | Academia Biology |
Online Access: | https://www.academia.edu/123385504/LASSO_MOGAT_a_multi_omics_graph_attention_framework_for_cancer_classification |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs
by: Samuel S. Boyd, et al.
Published: (2025-02-01) -
Risk Assessment of Renewable Energy Power Systems via Graph Multi-Attention Networks
by: BAI Yunpeng, ZHANG Zhiyan, XU Cai, GUO Chuangxin, LIU Zhuping, ZHU Wenhao
Published: (2025-01-01) -
GTAT: empowering graph neural networks with cross attention
by: Jiahao Shen, et al.
Published: (2025-02-01) -
Synchronization-based graph spatio-temporal attention network for seizure prediction
by: Jie Xiang, et al.
Published: (2025-02-01) -
Graph attention convolution network for power flow calculation considering grid uncertainty
by: Haochen Li, et al.
Published: (2025-04-01)