Automated analysis of high‐content microscopy data with deep learning-Reference-Cited by-同舟云学术

Automated analysis of high‐content microscopy data with deep learning

Published:2017-04 Issue:4 Volume:13 Page:924
ISSN:1744-4292
Container-title:Molecular Systems Biology
language:en
Short-container-title:Mol Syst Biol

Author:

Kraus Oren Z¹²^ORCID,Grys Ben T²³,Ba Jimmy¹,Chong Yolanda⁴,Frey Brendan J¹²⁵⁶,Boone Charles²³⁵,Andrews Brenda J²³⁵^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada

2. Donnelly Centre for Cellular and Biomolecular Research University of Toronto Toronto ON Canada

3. Department of Molecular Genetics University of Toronto Toronto ON Canada

4. Cellular Pharmacology Discovery Sciences Janssen Pharmaceutical Companies Johnson & Johnson Beerse Belgium

5. Canadian Institute for Advanced Research Program on Genetic Networks Toronto ON Canada

6. Canadian Institute for Advanced Research Program on Learning in Machines & Brains Toronto ON Canada

Funder

Canada Foundation for Innovation

Ministry of Research and Innovation

Canadian Institutes of Health Research

National Institutes of Health

Connaught Fund

Publisher

EMBO

Subject

Applied Mathematics,Computational Theory and Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Information Systems

Link

https://onlinelibrary.wiley.com/doi/pdf/10.15252/msb.20177551

Reference53 articles.

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2. A novel single-cell screening platform reveals proteome plasticity during yeast stress responses

3. The emergence of proteome-wide technologies: systematic analysis of proteins comes of age

4. Caffe Model Zoo(2016) Available at:https://github.com/BVLC/caffe/wiki/Model-Zoo

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