MOGAT: An Improved Multi-Omics Integration Framework Using Graph Attention Networks

Integration of multi-omics data holds great promise for understanding the complex biology of diseases, particularly Alzheimers, Parkinsons, and cancer. However, the integration is challenging due to the high dimensionality and complexity of the data. Traditional machine learning methods are not well-suited for handling the complex relationships between different types of omics data. Many models were proposed that utilize graph-based learning models to extract hidden representations and network structures from different omics data to enhance cancer prediction, patient categorization, etc. The existing graph neural network-based (GNN-based) multi-omics approaches for cancer subtype prediction have three shortcomings: (a) Do not consider all types of omics data, (b) Fail to determine the relative significance of the neighboring nodes (in this case, samples or patients) when it comes to downstream analyses, such as subtype classification, patient stratification, etc., and (c) Use the same approach for generating initial graphs for different omics data. To overcome these shortcomings, we present MOGAT, a novel multi-omics integration approach, leveraging a graph attention network (GAT) model that incorporates graph-based learning with an attention mechanism. MOGAT utilizes a multi-head attention mechanism that can more efficiently extract information for a specific sample by assigning unique attention coefficients to its neighboring samples. To evaluate the performance of MOGAT, we explored its capability via a case study of predicting breast cancer subtypes. Our results showed that MOGAT performs better than the state-of-the-art multi-omics integration approaches.