A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks

A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks

Abstract Background and objectives Comprehensive analysis of multi-omics data is crucial for accurately formulating effective treatment plans for complex diseases. Supervised ensemble methods have gained popularity in recent years for multi-omics data analysis. However, existing research based on su...

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Bibliographic Details
Main Authors: Jiahui Wang, Nanqing Liao, Xiaofei Du, Qingfeng Chen, Bizhong Wei
Format: Article
Language:English
Published: BMC 2024-01-01
Series:BMC Genomics
Subjects:
Multi-omics
Semi-supervised learning
Multi-head self-attention mechanism
Graph Convolutional Networks
Online Access:https://doi.org/10.1186/s12864-024-09985-7

Internet

https://doi.org/10.1186/s12864-024-09985-7

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