A similarity regression fusion model for integrating multi-omics data to identify cancer subtypes

The identification of cancer subtypes is crucial to cancer diagnosis and treatments. A number of methods have been proposed to identify cancer subtypes by integrating multi-omics data in recent years. However, the existing methods rarely consider the biases of similarity between samples and weights of different omics data in integration. More accurate and flexible integration approaches need to be developed to comprehensively investigate cancer subtypes. In this paper, we propose a simple and flexible similarity fusion model for integrating multi-omics data to identify cancer subtypes. We consider the similarity biases between samples in each omics data and predict corrected similarities between samples using a generalized linear model. We integrate the corrected similarity information from multi-omics data according to different data-view weights. Based on the integrative similarity information, we cluster patient samples into different subtype groups. Comprehensive experiments demonstrate that the proposed approach obtains more significant results than the state-of-the-art integrative methods. In conclusion, our approach provides an effective and flexible tool to investigate subtypes in cancer by integrating multi-omics data.