Abstract
The integration of multi-omics data presents a major challenge in precision
medicine, requiring advanced computational methods for accurate disease
classification and biological interpretation. This study introduces the
Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN), a deep learning model
that integrates messenger RNA, micro RNA sequences, and DNA methylation data
with Protein-Protein Interaction (PPI) networks for accurate and interpretable
cancer classification across 31 cancer types. MOGKAN employs a hybrid approach
combining differential expression with DESeq2, Linear Models for Microarray
(LIMMA), and Least Absolute Shrinkage and Selection Operator (LASSO) regression
to reduce multi-omics data dimensionality while preserving relevant biological
features. The model architecture is based on the Kolmogorov-Arnold theorem
principle, using trainable univariate functions to enhance interpretability and
feature analysis. MOGKAN achieves classification accuracy of 96.28 percent and
demonstrates low experimental variability with a standard deviation that is
reduced by 1.58 to 7.30 percents compared to Convolutional Neural Networks
(CNNs) and Graph Neural Networks (GNNs). The biomarkers identified by MOGKAN
have been validated as cancer-related markers through Gene Ontology (GO) and
Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The
proposed model presents an ability to uncover molecular oncogenesis mechanisms
by detecting phosphoinositide-binding substances and regulating sphingolipid
cellular processes. By integrating multi-omics data with graph-based deep
learning, our proposed approach demonstrates superior predictive performance
and interpretability that has the potential to enhance the translation of
complex multi-omics data into clinically actionable cancer diagnostics.