Zebrafish Larva Locomotor Activity Analysis Using Machine Learning Techniques

Zebra fish larvae have become a popular model organism to investigate genetic and environmental factors affecting behavior. However, difficulties exist in the analysis of complex behaviors from a large array of larvae. In this paper, we present the new application of machine learning techniques in bioinformatics to automatically detect and investigate the locomotor activities of zebra fish larvae. To achieve this, twelve features were defined and seven unsupervised learning methods were implemented. Next, seven performance measures were applied to evaluate and compare these methods. In order to empirically evaluate the machine learning algorithms, a large dataset was collected that contained 6847 valid instances. Using this dataset, the characteristics of the features were analyzed and the most appropriate unsupervised learning algorithm, i.e., Unweighted Pair Group Method with Arithmetic mean (UPGMA), for locomotor activity analysis was identified. In addition, UPGMA's ability to reveal underlying patterns of zebra fish locomotor activities was demonstrated. In general, this study shows that machine learning techniques have the potential to construct effective, high-throughput systems to automate the process of identifying zebra fish behaviors influenced by genetic manipulation, pharmaceuticals, and environmental toxins.