Computational classification of different wild-type zebrafish strains based on their variation in light-induced locomotor response

Yuan Gao, Gaonan Zhang, Beth Jelfs, Robert Carmer, Prahatha Venkatraman, Mohammad Ghadami, Skye A. Brown, Chi Pui Pang, Yuk Fai Leung, Rosa H.M. Chan, Mingzhi Zhang

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

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 30 after 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.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalComputers in Biology and Medicine
Volume69
DOIs
Publication statusPublished - 1 Feb 2016
Externally publishedYes

Bibliographical note

Funding Information:
Yuk Fai Leung is an Associate Professor in the Department of Biological Sciences at Purdue University. He received a B.Sc. (1st Hon.) degree and an M.Phil. degree in Biochemistry from the Hong Kong University of Science and Technology in 1996 and 1998 respectively. He received his Ph.D. degree in Ophthalmology from the Chinese University of Hong Kong in 2002. Dr. Leung was awarded with a Croucher Foundation Postdoctoral Fellowship the same year and pursued his postdoctoral research at Harvard University until 2007. In 2005, he was awarded with a Paediatric Ophthalmology Research Grant from the Knights Templar Eye Foundation. In 2008, Dr. Leung established his own research group at Purdue University in the Department of Biological Sciences. He also received a Hope for Vision Award in the same year. His current research focuses on using zebrafish eye disease models to elucidate disease-causing gene network and identify new drug therapies.

Funding Information:
The work described in this paper was substantially supported by a Grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project no. CityU 123312 ) and Grants from City University of Hong Kong (Project no. 7200275 and 7004437 ). G. Zhang was partially supported by a William H. Phillips Summer Research Internship from Purdue University . R. Carmer was partially supported by a Howard Hughes Medical Institute Undergraduate Research Experience Program from Purdue University . P. Venkatraman was partially supported by a Faculty for the Future Fellowship by the Schlumberger Foundation . C.P. Pang was partially supported by a Direct Grant (Grant no. 2041771 ) from the Medical Panel, The Chinese University of Hong Kong , and a General Research Fund (Grant no. 2140694 ) from the Research Grants Council of Hong Kong . M. Zhang was partially supported by the National Scientific Foundation of China (Grant no. 81486126 ), the Provincial Natural Scientific Foundation of China (Grant no. 8151503102000019 ), and the Ministry of Health Program of Public Welfare (Grant no. 201302015 ).

Publisher Copyright:
© 2015 Elsevier Ltd.

Keywords

  • Computational classification
  • Light-induced locomotor response
  • Support vector machines
  • Visual motor response
  • Zebrafish

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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