Multi-Kernel Fusion with Fuzzy Label Relaxation for Predicting Distant Metastasis in Nasopharyngeal Carcinoma
The heterogeneity of omics data poses a challenge for feature fusion in the medical field due to source differences. This study aims to construct a fusion method that can reduce the differences between omics data, enabling them to jointly contribute to specific medical tasks. The multi-kernel late-fusion method is capable of reducing the impact of these differences by mapping the features using the most suitable single-kernel function and then combining them in a high-dimensional space that can effectively represent the data. However, the strict label fitting of complex nasopharyngeal carcinoma (NPC) data samples restricts the performance of general classifiers when using highdimensional features. To address this issue, this study proposes a multi-kernel model for multi-omics feature fusion in predicting distant metastasis of NPC patients. The proposed model employs a multi-kernel-based Radial basis function (RBF) neural network and introduces a label fuzzy softening method to enlarge the margin between two classes. By mapping the original medical omics data and reducing the differences, the proposed method provides more degrees of freedom for label fitting, improving the classification ability. The proposed model is evaluated on multi-omics datasets, and the results demonstrate its strength and effectiveness in predicting distant metastasis of NPC patients.
