Multiple Query-based Multi-omics Fusion Algorithm for Cancer Classification and Metastasis Prediction

The research on cancer classification and metastasis prediction based on multi-omics data has emerged with the development of high-throughput sequencing techniques and artificial intelligence. However, effective fusion among omics data remains a challenging problem, besides the ’curse of dimensionality’ resulting from high-dimensional features and small sample sizes. The transform-based fusion methods commonly used in computer vision and natural language processing require abundant samples for learning, which can lead to overfitting problems when applied in the biomedical field. Thus, this paper proposes a multiple query-based fusion algorithm with fewer parameters to learn, while retaining effective integration abilities for multiple omics data. It consists of interaction and fusion modules, which can prevent the model from training too aggressively and effectively mitigate overfitting, thus enabling the fusion of small datasets. Furthermore, this paper introduces a multi-task learning model based on it for cancer classification and metastasis prediction tasks. The proposed fusion algorithm can generalize to multiple omics data and maintain stable parameter counts. Experimental results demonstrate that the proposed method outperforms other state-of-the-art supervised multi-omics integrative methods in various cancer classification applications using DNA methylation, RNA expression, and miRNA expression data.