MOKAN: A Multi-Omics Data Analysis Framework Using Kolmogorov-Arnold Networks

Jingguang He, Shunxin Xiao, Sujia Huang, Jianmin Li, Da-Han Wang

Abstract: With the advancement of high-throughput sequencing technology, large amounts of multi-omics data have been accumulated. Due to the comprehensive representation of different molecular layers, multi-omics data enable a deep understanding of cancer analysis, including subtype classification, biomarker identification and so on. However, existing multi-omics integration methods either rely on complex feature engineering or fail to capture the heterogeneity of multi-source data. To integrate multi-omics data in a complementary and collaborative manner, we propose a Kolmogorov-Arnold Networks-based multi-omics integration analysis framework, termed MOKAN. In detail, MOKAN first employs a sample-weighted random sampler to reduce the difference in the number of samples involved in training. Then, to perform this data analysis, our proposed model, MOKAN, leverages KAN’s architecture, utilizing its learnable edge-weight activation functions and flexible network structure. This design makes MOKAN particularly well-suited for handling multi-omics data by efficiently capturing diverse feature spaces across different omics layers. It integrates heterogeneous data types while preserving the unique contributions of each omics for comprehensive and accurate representation. Moreover, MOKAN employs KAN’s decomposition property to break down high-dimensional complex data into multiple low-dimensional subspaces, each independently capturing a specific aspect of the data. These subspace representations are then systematically integrated through a one-dimensional function that overlays and combines their contributions, effectively reconstructing the global data structure. Experimental results demonstrate that our proposed model outperforms other existing methods in cancer classification tasks.

External IDs:dblp:journals/titb/HeXHLW26