NTHRYS › Internships › Ai Bioprocess Optimization › Deep Learning for Bioprocess Anomaly Detection

Graph Neural Networks for Metabolic Network Topology Anomaly Detection

Ai Bioprocess Optimization

Deep Learning for Bioprocess Anomaly Detection

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Ai Bioprocess Optimization › Deep Learning for Bioprocess Anomaly Detection

Graph Neural Networks for Metabolic Network Topology Anomaly Detection

This study investigates GNN architectures that represent metabolic networks as dynamic graphs to detect topological anomalies indicating pathway dysregulation or metabolic imbalances during bioprocess operation. The research contributes fundamental advances in applying geometric deep learning to systems-level bioprocess monitoring and anomaly characterization.

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📚 Academic: Thesis & PPT assistance included🧪 Tech: Master the protocols hands-on📝 Research > 3 months: Publication co-authorship in a Scopus-indexed journal

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