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

Revolutionizing Telemedicine in Europe with Mona Mobile: The 5G-Enabled ICU System

Read now 

Celebrating a Milestone: The Launch of Clinomic AI Lab

Read now 

Three new senior hires will strengthen Clinomic in Finance, Customer Solutions and Sales

Read now