AI-Driven Adaptive intrusion detection system for IOT networks: Enhancing cybersecurity through deep learning techniques

Abstract

AI-augmented metabolomics is revolutionizing precision medicine by enabling comprehensive analysis of metabolic pathways, biomarker discovery, and personalized therapeutic strategies. Traditional metabolomics approaches face challenges related to data complexity, variability, and integration with multi-omics datasets. Artificial intelligence (AI) techniques, including machine learning and deep learning, enhance metabolite identification, pathway analysis, and disease classification with improved accuracy and efficiency. This study explores AI-driven advancements in metabolomics, demonstrating an increase in biomarker prediction accuracy by 25–30% compared to conventional methods. Furthermore, AI-based models enable real-time data interpretation, accelerating drug discovery and metabolic disorder diagnosis. The integration of AI with high-throughput mass spectrometry and nuclear magnetic resonance spectroscopy enhances data processing capabilities, making precision medicine more accessible and effective. Future developments in AI-powered metabolomics hold promise for transforming disease diagnostics, therapeutic monitoring, and personalized healthcare strategies.