MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification

MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification

Abstract Background The integration of multi-omics data through deep learning has greatly improved cancer subtype classification, particularly in feature learning and multi-omics data integration. However, key challenges remain in embedding sample structure information into the feature space and des...

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Bibliographic Details
Main Authors: Lei Cheng, Qian Huang, Zhengqun Zhu, Yanan Li, Shuguang Ge, Longzhen Zhang, Ping Gong
Format: Article
Language:English
Published: BMC 2024-11-01
Series:BMC Bioinformatics
Subjects:
Adaptive multi-omics integration
Graph learning
Graph convolution network
Self-attention
Cancer subtype classification
Online Access:https://doi.org/10.1186/s12859-024-05989-y
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https://doi.org/10.1186/s12859-024-05989-y

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