  This paper presents, evaluates, and discusses a new software tool to
automatically build Dynamic Bayesian Networks (DBNs) from ordinary differential
equations (ODEs) entered by the user. The DBNs generated from ODE models can
handle both data uncertainty and model uncertainty in a principled manner. The
application, named PROFET, can be used for temporal data mining with noisy or
missing variables. It enables automatic re-estimation of model parameters using
temporal evidence in the form of data streams. For temporal inference, PROFET
includes both standard fixed time step particle filtering and its extension,
adaptive-time particle filtering algorithms. Adaptive-time particle filtering
enables the DBN to automatically adapt its time step length to match the
dynamics of the model. We demonstrate PROFET's functionality by using it to
infer the model variables by estimating the model parameters of four benchmark
ODE systems. From the generation of the DBN model to temporal inference, the
entire process is automated and is delivered as an open-source
platform-independent software application with a comprehensive user interface.
PROFET is released under the Apache License 2.0. Its source code, executable
and documentation are available at http:://profet.it.nuigalway.ie.
