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Evaluation of colorectal cancer subtypes and cell lines using deep learning

View ORCID ProfileJonathan Ronen, View ORCID ProfileSikander Hayat, View ORCID ProfileAltuna Akalin
doi: https://doi.org/10.1101/464743
Jonathan Ronen
1Max Delbrueck Center for Molecular Medicine, Berlin Institute for Medical Systems Biology, Bioinformatics Platform, Berlin, 13125, Germany
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Sikander Hayat
2Bayer AG, Department of Bioinformatics, 13353 Berlin, Germany
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Altuna Akalin
1Max Delbrueck Center for Molecular Medicine, Berlin Institute for Medical Systems Biology, Bioinformatics Platform, Berlin, 13125, Germany
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  • For correspondence: altuna.akalin@mdc-berlin.de
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Article Information

doi 
https://doi.org/10.1101/464743
History 
  • November 12, 2018.

Article Versions

  • Version 1 (November 7, 2018 - 15:29).
  • You are viewing Version 2, the most recent version of this article.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

Author Information

  1. Jonathan Ronen1,
  2. Sikander Hayat2 and
  3. Altuna Akalin1,*
  1. 1Max Delbrueck Center for Molecular Medicine, Berlin Institute for Medical Systems Biology, Bioinformatics Platform, Berlin, 13125, Germany
  2. 2Bayer AG, Department of Bioinformatics, 13353 Berlin, Germany
  1. ↵*corresponding author: altuna.akalin{at}mdc-berlin.de
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Posted November 12, 2018.
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Evaluation of colorectal cancer subtypes and cell lines using deep learning
Jonathan Ronen, Sikander Hayat, Altuna Akalin
bioRxiv 464743; doi: https://doi.org/10.1101/464743
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Evaluation of colorectal cancer subtypes and cell lines using deep learning
Jonathan Ronen, Sikander Hayat, Altuna Akalin
bioRxiv 464743; doi: https://doi.org/10.1101/464743

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