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Automated learning of generative models for subcellular location: Building blocks for systems biology†

  1. Ting Zhao1,2,
  2. Robert F. Murphy3,4,5,*

Article first published online: 30 OCT 2007

DOI: 10.1002/cyto.a.20487

Issue

Cytometry Part A

Cytometry Part A

Volume 71A, Issue 12, pages 978–990, December 2007

Additional Information

How to Cite

Zhao, T. and Murphy, R. F. (2007), Automated learning of generative models for subcellular location: Building blocks for systems biology. Cytometry, 71A: 978–990. doi: 10.1002/cyto.a.20487

Author Information

  1. 1

    Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

  2. 2

    Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

  3. 3

    Molecular Biosensor and Imaging Center, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

  4. 4

    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

  5. 5

    Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

*Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA

  1. †

    This work was presented at the XXIII Congress of the International Society for Analytical Cytology, Quebec City, Canada, 20–24 May 2006

Publication History

  1. Issue published online: 18 NOV 2007
  2. Article first published online: 30 OCT 2007
  3. Manuscript Accepted: 8 OCT 2007
  4. Manuscript Received: 11 SEP 2007

Funded by

  • NSF. Grant Number: EF-0331657
  • NIH. Grant Numbers: U54 RR022241, U54 DA021519

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