  Zebrafish are a common model organism used to identify new disease
therapeutics. High-throughput drug screens can be performed on larval zebrafish
in multi-well plates by observing changes in behaviour following a treatment.
Analysis of this behaviour can be difficult, however, due to the high
dimensionality of the data obtained. Statistical analysis of individual
statistics (such as the distance travelled) is generally not powerful enough to
detect meaningful differences between treatment groups. Here, we propose a
method for classifying zebrafish models of Parkinson's disease by genotype at 5
days old. Using a set of 2D behavioural features, we train a multi-layer
perceptron neural network. We further show that the use of integrated gradients
can give insight into the impact of each behaviour feature on genotype
classifications by the model. In this way, we provide a novel pipeline for
classifying zebrafish larvae, beginning with feature preparation and ending
with an impact analysis of said features.
