Topology Inference for Immune System Networks by Using Cell Amount Data

What the math gives to ML

The paper offers a constructive topology-inference mechanism based on three measurable requirements: nonnegative states, convergence to a finite equilibrium with prescribed component ratios, and signed interaction constraints. Its transferable asset is the conversion of these qualitative properties into a constrained quadratic program with explicit dynamical guarantees. A useful neural-network transfer is a positive, ratio-stable recurrent or state-space module whose transition matrix is fitted or projected under sign, contraction, and equilibrium constraints.