<p>Computational approaches to assessing clinical relevance of pre-clinical cancer models </p>

Vladimir Uzun; Ian Sudbery; Neil Lawrence; James Bradford

doi:10.7490/f1000research.1114651.1

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Uzun V, Sudbery I, Lawrence N and Bradford J. Computational approaches to assessing clinical relevance of pre-clinical cancer models [version 1; not peer reviewed]. F1000Research 2017, 6(ISCB Comm J):1397 (poster) (https://doi.org/10.7490/f1000research.1114651.1)

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Computational approaches to assessing clinical relevance of pre-clinical cancer models

Vladimir Uzun¹, Ian Sudbery, Neil Lawrence, James Bradford

Published 09 Aug 2017 (https://doi.org/10.7490/f1000research.1114651.1)

Author Affiliations

¹ University of Sheffield, UK

Copyright © 2017 Uzun V et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 09 Aug 2017

Computational approaches to assessing clinical relevance of pre-clinical cancer models
[version 1; not peer reviewed]

Vladimir Uzun¹, Ian Sudbery, Neil Lawrence, James Bradford

Author Affiliations

¹ University of Sheffield, UK

Presented at

Joint 25th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and 16th European Conference on Computational Biology (ECCB) 2017

Competing Interests

No competing interests were disclosed

Keywords

Cancer models, pre-clinical cancer models, cancer modelling, cancer cell lines, machine learning, cancer, tcga, ccle

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