Hybrid Multi-Modal Ai Approach for Depression Detection Using Predator-Prey Dynamics

Abstract

This study presents a hybrid multi-modal AI approach for depres- sion detection, integrating genomic network analysis with predator-prey dynamics. The proposed framework combines artificial intelligence tech- niques—including graph theory, information theory, and predictive mod- eling—with the Lotka-Volterra equations to analyze complex interactions between genetic, behavioral, and physiological data. The predator-prey model captures dynamic feedback within gene-behavior systems, offer- ing deeper insights into regulatory mechanisms influencing mental health. This AI-driven framework facilitates functional gene annotation, identifies disease associations, and supports drug discovery. Demonstrated through case studies in cancer, neurodegenerative, and infectious diseases, the ap- proach underscores its potential in personalized medicine and intelligent therapeutic innovation.