PUBLICATION

A High-Throughput Zebrafish Screening Method for Visual Mutants by Light-Induced Locomotor Response

Authors
Gao, Y., Chan, R.H., Chow, T.W., Zhang, L., Bonilla, S., Pang, C.P., Zhang, M., Leung, Y.F.
ID
ZDB-PUB-170214-318
Date
2014
Source
IEEE/ACM transactions on computational biology and bioinformatics   11(4): 693-701 (Journal)
Registered Authors
Bonilla, Sylvia, Leung, Yuk Fai, Zhang, Liyun
Keywords
none
MeSH Terms
  • Animals
  • High-Throughput Screening Assays/methods*
  • Larva/classification*
  • Larva/radiation effects
  • Machine Learning
  • Motor Activity/radiation effects*
  • Mutation
  • Reproducibility of Results
  • Vision Disorders/classification*
  • Zebrafish/classification*
  • Zebrafish/genetics
PubMed
26356340 Full text @ IEEE/ACM Trans Comput Biol Bioinform
Abstract
Normal and visually-impaired zebrafish larvae have differentiable light-induced locomotor response (LLR), which is composed of visual and non-visual components. It is recently demonstrated that differences in the acute phase of the LLR, also known as the visual motor response (VMR), can be utilized to evaluate new eye drugs. However, most of the previous studies focused on the average LLR activity of a particular genotype, which left information that could address differences in individual zebrafish development unattended. In this study, machine learning techniques were employed to distinguish not only zebrafish larvae of different genotypes, but also different batches, based on their response to light stimuli. This approach allows us to perform efficient high-throughput zebrafish screening with relatively simple preparations. Following the general machine learning framework, some discriminative features were first extracted from the behavioral data. Both unsupervised and supervised learning algorithms were implemented for the classification of zebrafish of different genotypes and batches. The accuracy of the classification in genotype was over 80 percent and could achieve up to 95 percent in some cases. The results obtained shed light on the potential of using machine learning techniques for analyzing behavioral data of zebrafish, which may enhance the reliability of high-throughput drug screening.
Genes / Markers
Figures
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping