Looking to join consortium for HORIZON-MISS-2026-02-CANCER-02

We can contribute unique expertise in microbiome network science, individualized microbiome analysis, and AI-driven predictive modeling for early cancer detection. Our work has uncovered fundamental principles governing microbial community dynamics, including universal interaction structures across individuals, complexity–stability trade-offs, and top-down identification of keystone taxa within microbial ecosystems. Building on these foundations, we developed innovative single-sample methodologies such as IDOA and individualized network analysis, enabling the characterization of microbial ecological states and dysbiosis from a single microbiome sample, an essential capability for clinically deployable diagnostics. In addition, our model-free machine learning frameworks, including kNN-based predictive approaches, provide scalable and interpretable tools for personalized risk assessment and prediction of microbiome responses to perturbations. Within the proposed call, these approaches could support the development of AI-assisted microbiome biomarkers for early cancer prediction, longitudinal monitoring of dysbiosis trajectories preceding disease onset, and personalized risk modeling integrating microbiome composition, ecological network structure, and clinical metadata across large patient cohorts.

Contact details: Dr. Amir Bashan Amir.bashan@gmail.com