Figure 1 . Schematic Representation of the Experimental Design.
A) Honeybees visited a feeder through a tunnel, used to alter their
distance perception (vertical stripes with respect to the direction of
flight increased, while horizontal stripes decreased the distance
perceived), and were then marked accordingly. B) At the observation
hive, honeybees were recorded while performing or not-performing dancing
behaviour and were finally prepared for RNAseq analysis (C). D) Three
machine learning algorithms (SVM, GLMNET and RFE) were trained on the
pre-processed sequence reads (Training Classifiers). E) Key features
from each model were compared to identify common elements (genes or
predictors).
Table 1 . Benchmark Algorithms. We chose to test SVM, GLMNET, RF
and RFE for our study, based on their use in previous research. The
first three algorithms use embedded feature selection (FS) to obtain key
predictors from the trained model (Embedded), while RFE requires an
underlying embedded approach for the ranking (Wrapper). We report the
studies that featured or reviewed these algorithms.