Multi-Omics Data Integration Patient Classification Method Based on Deep Dense Residual Shrinkage Network

Wenyao Li, Kai Lin, Yaqing Hou, Qiang Zhang

Abstract: Represented by high-throughput sequencing technology, rapid advances in omics technologies have allowed scholars to understand human diseases in a more profound and comprehensive manner. However, omics data are difficult to analyze accurately on account of their characters: massive redundancy, full of noise and high dimension. Meanwhile, it is crucial to improve the performance of omics analysis from multiple modalities using data from different omics. To this end, we propose a novel multi-omics integration model for patient classification. Firstly, anti-noisy deep dense residual shrinkage neural networks (DDRSNN) are trained and utilized to make preliminary predictions on various omics data of patients. Then confidence scores are calculated for each sample through the confidence distance to evaluate the reliability of the preliminary prediction result. Finally, referring to uncertain evidence fusion theory, the preliminary prediction results from different omics are integrated based on confidence scores and the final patient classification is obtained. The algorithm combines multiple omics, treating each omics as evidence of a separate modality, and improves the accuracy and reliability of patient classification by integrating evidence from multiple modalities.