An AI-Driven Mathematical Modeling Framework for Personalized Cancer Therapy Using Genomic Networks and Deep Learning

Abstract

Cancer is a highly heterogeneous disease characterized by complex genetic alterations, molecular interactions, signaling pathways, and dynamic tumor evolution, which significantly influence disease progression and therapeutic response. Conventional treatment strategies often rely on generalized clinical and pathological characteristics and may not adequately capture patient-specific molecular differences. Therefore, there is a growing need for computational approaches capable of supporting personalized cancer-treatment decisions. This study proposes an artificial intelligence (AI)-driven framework that integrates mathematical modeling of genomic networks with deep learning for personalized cancer therapy. Patient-specific genomic and clinical information, including gene expression profiles, somatic mutations, copy-number alterations, and other molecular characteristics, is utilized to construct individualized genomic networks. Mathematical modeling is employed to quantify gene relationships, molecular interactions, and pathway structures and to derive biologically meaningful network-based features. These features are subsequently analyzed using deep learning techniques, particularly Graph Neural Networks (GNNs), to capture complex relationships between genomic characteristics and therapeutic outcomes. Multi-omics data integration is incorporated to provide a comprehensive representation of tumor biology and improve predictive performance. Furthermore, Explainable Artificial Intelligence (XAI) techniques are employed to identify key genes, signaling pathways, and network interactions contributing to treatment-response predictions. The proposed framework aims to predict treatment response and drug sensitivity while simultaneously providing interpretable biological insights. By integrating mathematical modeling, genomic network analysis, multi-omics data, deep learning, and explainable AI, this framework establishes a computational foundation for precision oncology and supports personalized cancer-treatment decision-making.