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

“Clusters4Future”: Funding für Clinomic and the cluster „NeuroSys“ from Aachen

Read now 

Healthcare and Data – WLAD Podcast episode #65

Read now 

5G am Patientenbett für hervorragende Konnektivität

Read now