Artificial Intelligence for Network-Based Oncomarker Discovery and Cancer Prediction

Dear Colleagues,

The discovery of reliable biomarkers, or oncomarkers, remains one of the central challenges in oncology, with profound implications for early detection, prognosis, and personalised treatment strategies. While traditional biomarker studies have provided important insights, they often focus on individual molecules rather than the complex biological networks driving cancer initiation and progression.

Recent advances in artificial intelligence (AI) and machine learning (ML) are enabling a paradigm shift toward network-based biomarker discovery. By integrating genomics, transcriptomics, proteomics, epigenomics, and metabolomics, AI methods can uncover novel molecular interactions, dysregulated pathways, and predictive signatures that may remain hidden in conventional analyses. In particular, graph neural networks (GNNs), network embeddings, and systems biology-inspired approaches hold promise for identifying prognostic and predictive oncomarkers, improving risk stratification, and informing clinical decision support in oncology.

The aim of this Special Issue is to bring together cutting-edge research and comprehensive reviews on the applications of AI for network-based biomarker discovery and cancer prediction. We welcome contributions that explore computational innovations and translational insights with clinical relevance.

Dr. Oleg Blyuss
Guest Editor

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