An Adaptive Learning Approach To Parameter Estimation For Hybrid Petri Nets In Systems Biology

Petri nets (HPNs) that can model biological systems. In particular, based on a state space formulation we develop a decisionaided adaptive gradient descent (DAAGD) algorithm capable of cost-effectively estimating the parameters used in an HPN model. Contrary to standard gradient descent techniques, the DAAGD algorithm does not require prior knowledge, i.e., information about the discrete transitions’ firing instants. Simulations of a gene regulatory network assess the performance of the proposed DAAGD algorithm against standard gradient descent algorithms with full, imperfect and no prior knowledge.

Request our conference paper via our contact form!

More stories

Internationaler Tag der Pflegenden: mehr Unterstützung in der Patientenbehandlung

Read now 
Georg Griesemann joins Clinomic

Georg Griesemann appointed as new CEO of Clinomic

Read now 

Clinomic TDMS für eine qualitative Telemedizin

Read now