Cross-domain structural pattern
Proteome-metabolome dynamics are modeled as continuous interplay via Neural ODEs. Rather than requiring pre-specified mechanistic knowledge, NODEs infer latent interaction structure directly from time-series multiomics data. Learned continuous dynamics enable simulation of temporal trajectories and anticipation of intervention effects, capturing complex feedback between protein expression and metabolite concentrations without manually encoding pathway topology.
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