Multilayer Perceptron Network Discriminates Larval Zebrafish Genotype using Behaviour
This provides a novel pipeline for high-throughput drug screening in zebrafish, addressing the challenge of high-dimensional behavioral data analysis, though it is incremental as it applies an existing method to a specific domain.
The researchers tackled the problem of detecting meaningful differences in larval zebrafish behavior for drug screening by using a multilayer perceptron neural network to classify genotypes, achieving classification of Parkinson's disease models at 5 days old with integrated gradients for feature impact analysis.
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.