  Discovering a meaningful symbolic expression that explains experimental data
is a fundamental challenge in many scientific fields. We present a novel,
open-source computational framework called Scientist-Machine Equation Detector
(SciMED), which integrates scientific discipline wisdom in a
scientist-in-the-loop approach, with state-of-the-art symbolic regression (SR)
methods. SciMED combines a wrapper selection method, that is based on a genetic
algorithm, with automatic machine learning and two levels of SR methods. We
test SciMED on five configurations of a settling sphere, with and without
aerodynamic non-linear drag force, and with excessive noise in the
measurements. We show that SciMED is sufficiently robust to discover the
correct physically meaningful symbolic expressions from the data, and
demonstrate how the integration of domain knowledge enhances its performance.
Our results indicate better performance on these tasks than the
state-of-the-art SR software packages , even in cases where no knowledge is
integrated. Moreover, we demonstrate how SciMED can alert the user about
possible missing features, unlike the majority of current SR systems.
