MOSGAT: Uniting Specificity-Aware GATs and Cross Modal-Attention to Integrate Multi-Omics Data for Disease Diagnosis

With the advancement of sequencing methodologies, the acquisition of vast amounts of multi-omics data presents a significant opportunity for comprehending the intricate biological mechanisms underlying diseases and achieving precise diagnosis and treatment for complex disorders. However, as diverse...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2024-09, Vol.28 (9), p.5624-5637
Main Authors: Wu, Wenhao, Wang, Shudong, Zhang, Yuanyuan, Yin, Wenjing, Zhao, Yawu, Pang, Shanchen
Format: Article
Language:English
Subjects:
Algorithms
Bioinformatics
Computational Biology - methods
Cross modal-attention
Data integration
Data mining
Databases, Factual
disease diagnosis
Diseases
Feature extraction
Genomics - methods
Humans
Medical diagnosis
multi-omics data integration
Multiomics
Neural networks
sample-specific features
specificity-aware GATs
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Summary:With the advancement of sequencing methodologies, the acquisition of vast amounts of multi-omics data presents a significant opportunity for comprehending the intricate biological mechanisms underlying diseases and achieving precise diagnosis and treatment for complex disorders. However, as diverse omics data are integrated, extracting sample-specific features within each omics modality and exploring potential correlations among different modalities while avoiding mutual interference becomes a critical challenge in multi-omics data integration research. In the context of this study, we proposed a framework that unites specificity-aware GATs and cross-modal attention to integrate different omics data (MOSGAT). To be specific, we devise Graph Attention Networks (GATs) tailored for each omics modality data to perform feature extraction on samples. Additionally, an adaptive confidence attention weighting technique is incorporated to enhance the confidence in the extracted features. Finally, a cross-modal attention mechanism was devised based on multi-head self-attention, thoroughly uncovering potential correlations between different omics data. Extensive experiments were conducted on four publicly available medical datasets, highlighting the superiority of the proposed framework when compared to state-of-the-art methodologies, particularly in the realm of classification tasks. The experimental results underscore MOSGAT's effectiveness in extracting features and exploring potential inter-omics associations.
ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2024.3415641