Title
Computational classification of different wild-type zebrafish strains based on their variation in light-induced locomotor response.
Abstract
Zebrafish larvae display a rapid and characteristic swimming behaviour after abrupt light onset or offset. This light-induced locomotor response (LLR) has been widely used for behavioural research and drug screening. However, the locomotor responses have long been shown to be different between different wild-type (WT) strains. Thus, it is critical to define the differences in the WT LLR to facilitate accurate interpretation of behavioural data. In this investigation, we used support vector machine (SVM) models to classify LLR data collected from three WT strains: AB, TL and TLAB (a hybrid of AB and TL), during early embryogenesis, from 3 to 9 days post-fertilisation (dpf). We analysed both the complete dataset and a subset of the data during the first 30after light change. This initial period of activity is substantially driven by vision, and is also known as the visual motor response (VMR). The analyses have resulted in three major conclusions: First, the LLR is different between the three WT strains, and at different developmental stages. Second, the distinguishable information in the VMR is comparable to, if not better than, the full dataset for classification purposes. Third, the distinguishable information of WT strains in the light-onset response differs from that in the light-offset response. While the classification accuracies were higher for the light-offset than light-onset response when using the complete LLR dataset, a reverse trend was observed when using a shorter VMR dataset. Together, our results indicate that one should use caution when extrapolating interpretations of LLR/VMR obtained from one WT strain to another.
Year
DOI
Venue
2016
10.1016/j.compbiomed.2015.11.012
Computers in Biology and Medicine
Keywords
Field
DocType
Zebrafish,Computational classification,Light-induced locomotor response,Visual motor response,Support vector machines
Swimming behaviour,Pattern recognition,Computer science,Zebrafish,Artificial intelligence,Wild type,Use caution
Journal
Volume
Issue
ISSN
69
C
0010-4825
Citations 
PageRank 
References 
0
0.34
2
Authors
11
Name
Order
Citations
PageRank
Yuan Gao126447.87
Gaonan Zhang200.34
Beth Jelfs3629.40
Robert Carmer400.34
Prahatha Venkatraman500.34
Mohammad Ghadami600.34
Skye A. Brown700.34
Chi Pui Pang800.34
Yuk Fai Leung900.68
Rosa H M Chan1018222.79
Zhang Mingzhi1111.14