Multilayer Perceptron Network Discriminates Larval Zebrafish Genotype using Behaviour

Autor:	Fusco, Christopher, Allen, Angel
Rok vydání:	2022
Předmět:	Quantitative Biology - Quantitative Methods Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Preprint
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.03051 Zobrazit plný text záznamu View this record from Arxiv