• 1
    Stern HM,Zon LI. Cancer genetics and drug discovery in the zebrafish. Nature Reviews Cancer 2003; 3: 533539.
  • 2
    Mayden RL,Tang KL,Conway KW,Freyhof J,Chamberlain S,Haskins M,Schneider L,Sudkamp M,Wood RM,Agnew M, et al. Phylogenetic relationships of Danio within the order Cypriniformes: A framework for comparative and evolutionary studies of a model species. J Exp Zool B Mol Dev Evol 2007; 308B: 642654.
  • 3
    Xiang J,Yang H,Che C,Zou H,Yang H,Wei Y,Quan J,Zhang H,Yang Z,Lin S. Identifying tumor cell growth inhibitors by combinatorial chemistry and zebrafish assays. PLoS ONE 2009; 4: e4361.
  • 4
    Major RJ,Poss KD. Zebrafish heart regeneration as a model for cardiac tissue repair. Drug Discov Today Dis Models 2007; 4: 219225.
  • 5
    Tsujikawa M,Malicki J. Intraflagellar transport genes are essential for differentiation and survival of vertebrate sensory neurons. Neuron 2004; 42: 703716.
  • 6
    Holley SA,Geisler R,Nüsslein-Volhard C. Control of her1 expression during zebrafish somitogenesis by a delta-dependent oscillator and an independent wave-front activity. Genes Dev 2000; 14: 16781690.
  • 7
    Jülich D,Lim CH,Round J,Nicolaije C,Schroeder J,Davies A,Geisler R,Lewis J,Jiang Y,Holley SA. beamter/deltaC and the role of Notch ligands in the zebrafish somite segmentation, hindbrain neurogenesis and hypochord differentiation. Dev Biol 2005; 286: 391404.
  • 8
    Mara A,Schroeder J,Chalouni C,Holley SA. Priming, initiation and synchronization of the segmentation clock by deltaD and deltaC. Nat Cell Biol 2007; 9: 523530.
  • 9
    Liu T,Nie J,Li G,Guo L,Wong STC. ZFIQ: A software package for zebrafish biology. Bioinformatics 2008; 24: 438439.
  • 10
    Liu T,Lu J,Wang Y,Campbell WA,Huang L,Zhu J,Xia W,Wong STC. Computerized image analysis for quantitative neuronal phenotyping in zebrafish. J Neurosci Methods 2006; 153: 190202.
  • 11
    Li G,Liu T,Nie J,Guo L,Malicki J,Mara A,Holley SA,Xia W,Wong STC. Detection of blob objects in microscopic zebrafish images based on gradient vector diffusion. Cytometry Part A 2007; 71A: 835845.
  • 12
    Li G,Liu T,Nie J,Guo L,Chen J,Zhu J,Xia W,Mara A,Holley S,Wong STC. Segmentation of touching cell nuclei using gradient flow tracking. J Microsc 2008; 231: 4758.
  • 13
    Boland MV,Murphy RF. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 2001; 17: 12131223.
  • 14
    Newberg JY,Li J,Rao A,Pontén F,Uhlén M,Lundberg E,Murphy RF. Automated analysis of human protein atlas immunofluorescence images. In: Proceedings of the Sixth IEEE International Conference on Symposium on Biomedical Imaging: From Nano to Macro. Piscataway: IEEE Press; 2009. pp 10231026.
  • 15
    Wang J,Zhou X,Bradley PL,Chang SF,Perrimon N,Wong STC. Cellular phenotype recognition for high-content RNA interference genome-wide screening. J Biomol Screen 2008; 13: 2939.
  • 16
    Huh S,Lee D,Murphy RF. Efficient framework for automated classification of subcellular patterns in budding yeast. Cytometry Part A 2009; 75A: 934940.
  • 17
    Carpenter AE,Jones TR,Lamprecht MR,Clarke C,Kang IH,Friman O,Guertin DA,Chang JH,Lindquist RA,Moffat J, et al. CellProfiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol 2006; 7: R100.
  • 18
    Peng H,Long F,Zhou J,Leung G,Eisen MB,Myers EW. Automatic image analysis for gene expression patterns of fly embryos. BMC Cell Biol 2007; 8:S7.
  • 19
    Lu Y,Lu J,Liu T,Yang J. Automated cell phase classification for zebrafish fluorescence microscope images. In: Proceedings of 20th International Conference on Pattern Recognition. Washington: IEEE Computer Society; 2010. pp 25842587.
  • 20
    Haralick RM,Shanmugam K,Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973; 3: 610621.
  • 21
    Manjunath BS,Ma WY. Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 1996; 18: 837842.
  • 22
    Hamilton NA,Pantelic RS,Hanson KH,Teasdale RD. Fast automated cell phenotype image classification. BMC Bioinformatics 2007; 8: 110.
  • 23
    Fisher RA. The use of multiple measurements in taxonomic problems. Annal Eug 1936; 7: 179188.
  • 24
    Duda RO,Hart PE,Stork DG. Pattern classification, 2nd ed. New York: John Wiley & Sons Inc.; 2001. p 117.
  • 25
    Johnson AE,Hebert M. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans Pattern Anal Mach Intell 1999; 21: 433449.
  • 26
    Lazebnik S,Schmid C,Ponce J. A sparse texture representation using local affine regions. IEEE Trans Pattern Anal Mach Intell 2005; 27: 12651278.
  • 27
    Alpaydin E. Introduction to machine learning. London: The MIT Press; 2004. p 225.
  • 28
    Chang C,Lin C. LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2011; 2: 27.
  • 29
    Chapelle O,Haffner P,Vapnik VN. Support vector machines for histogram-based image classification. IEEE Trans Neural Netw 1999; 10: 10551064.
  • 30
    Jing F,Li M,Zhang H,Zhang B. Support vector machines for region-based image retrieval. In: Proceedings of 2003 International Conference on Multimedia and Expo. Washington: IEEE Computer Society Press; 2003. pp 2124.
  • 31
    Fowlkes C,Belongie S,Chung F,Malik J. Spectral grouping using the Nyström method. IEEE Trans Pattern Anal Mach Intell 2004; 26: 214225.
  • 32
    Zhang J,Marszalek M,Lazebnik L,Schmid C. Local features and kernels for classification of texture and object categories: A comprehensive study. Int J Comp Vision 2007; 73: 213238.