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A Semi-supervised Approach for the Integration of Multi-omics Data Based on Transformer Multi-head Self-attention Mechanism and Graph Convolutional Networks by Jiahui Wang; Nanqing Liao; Xiaofei Du; Qingfeng Chen; Bizhong Wei is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## 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 supervised learning algorithms often fails to fully harness the information from unlabeled nodes and overlooks the latent features within and among different omics, as well as the various associations among features. Here, we present a novel multi-omics integrative method MOSEGCN, based on the Transformer multi-head self-attention mechanism and Graph Convolutional Networks(GCN), with the aim of enhancing the accuracy of complex disease classification. MOSEGCN first employs the Transformer multi-head self-attention mechanism and Similarity Network Fusion (SNF) to separately learn the inherent correlations of latent features within and among different omics, constructing a comprehensive view of diseases. Subsequently, it feeds the learned crucial information into a self-ensembling Graph Convolutional Network (SEGCN) built upon semi-supervised learning methods for training and testing, facilitating

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Author

Jiahui Wang; Nanqing Liao; Xiaofei Du; Qingfeng Chen; Bizhong Wei

Publisher

BioMed Central

Published

2024

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)