MKLNID: Identifying Melanoma-related Pathogenic Genes Through Multiple Kernel Learning and Network Impulsive Dynamics.

Melanoma is a highly malignant skin cancer, and identifying its pathogenic genes is crucial for understanding its pathogenesis and developing treatment strategies. Network-based approaches effectively capture the synergistic interactions among genes and their products within biological systems, yet extracting functional insights from these complex networks remains challenging. Here, we propose a novel approach that combines multiple kernel learning and network impulsive dynamics (MKLNID) to predict melanoma-related pathogenic genes. Specifically, we construct similarity kernels of diseases and genes from the original disease-gene heterogeneous network and melanoma expression profiles. These kernels are integrated via multiple kernel learning to generate enhanced similarity networks for diseases and genes, respectively. Impulsive signals are then applied to specific nodes in the enhanced heterogeneous network, and the resulting dynamical response signatures are used to infer potential pathogenic genes. Comprehensive experiments and case analyses demonstrate the effectiveness of MKLNID in identifying melanoma-related genes. By deeply integrating heterogeneous disease networks with omics data and introducing network dynamics to simulate gene responses, MKLNID offers a new strategy for identifying melanoma-related genes, with potential implications for precision diagnosis and therapy.