Attention-Fusion Model for Multi-omics (AMMO) Data Integration in Lung Adenocarcinoma

Wentao Li, Amgad Muneer, Muhammad Waqas, Xiaobo Zhou, Jia Wu

Abstract: The multi-omics integration gives a whole new perspective into pathway analysis to reveal the complicated nature of cellular systems. While the understanding of interactions among different omics data remains unknown, current methods do not consider the unique and similar properties. In this paper, we propose Attention-fusion Model for Multi-Omics (AMMO), a robust method that addresses this challenge through domain separation. Our proposed attention-based approach inherently captures the similarities and differences across various omics modalities, enhancing the interpretability of the integrated data. Our proposed method can achieve a state-of-the-art C-index of 0.8017 in overall survival prediction in TCGA-LUAD data with the diverse types of omics data: DNA Methylation, exon expression RNA Seq (HiC), and protein expression (RPPA). We also demonstrated the performance increase by adding more modalities with the ablation test, the results confirmed our assumption of improving model performance by including more modalities to our method.

External IDs:dblp:conf/cmmca/LiM00024