Incremental Parameter Estimation of Stochastic State-Based Models

IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000317–000322

Abstract

This paper presents an incremental learning approach for estimating the structural parameters in stochastic state-based models (SSMs). SSMs have proven to be useful for modelling biological and medical processes, as they can represent both time dependency and stochastic processes. A major challenge in modelling in bioinformatics is that learning processes usually rely on large publicly accessible databases. In this work, a new approach is presented, where models are trained incrementally locally at different data sources, e.g., hospitals, without having to pass on sensitive data. After learning, only the parameters of the model are passed on, in this case the arc weights of stochastic Petri nets. As a result, data protection and privacy of patients in hospitals are respected and it is no longer necessary to rely on the existence of a suitable accessible database. Simulations are used to evaluate the performance of the algorithm for a gene regulatory network.

Authors 6

1. Anke Schmeink Aachen Research Area ISEK

   RWTH Aachen University

   Affiliation as printed

   Research Area ISEK, RWTH Aachen University, Germany

2. Guido Dartmann

   Trier University of Applied Sciences

   Affiliation as printed

   Environmental Campus, Trier University of Applied Sciences, Germany

3. Lejla Begic Fazlic

   Trier University of Applied Sciences

   Affiliation as printed

   Environmental Campus, Trier University of Applied Sciences, Germany

4. Thomas R. Vollmer
   Affiliation as printed

   Philips Research, Aachen, Germany

5. Arne Peine Aachen

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   University Hospital RWTH Aachen, Germany

6. Lukas Märtin Aachen

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   University Hospital RWTH Aachen, Germany