MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery
Wei Zhang, Minjie Mou, Wei Hu, Mingkun Lu, Hanyu Zhang, Hongning Zhang, Yongchao Luo, Hongquan Xu, Lin Tao, Haibin Dai, Jianqing Gao, Feng Zhu
Abstract
In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.