<p><i>In silico</i> prediction of novel therapeutic targets using gene disease association data</p>

Enrico Ferrero; Ian Dunham; Philippe Sanseau

doi:10.7490/f1000research.1114460.1

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Ferrero E, Dunham I and Sanseau P. In silico prediction of novel therapeutic targets using gene disease association data [version 1; not peer reviewed]. F1000Research 2017, 6(ISCB Comm J):1182 (poster) (https://doi.org/10.7490/f1000research.1114460.1)

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In silico prediction of novel therapeutic targets using gene disease association data

Enrico Ferrero¹, Ian Dunham, Philippe Sanseau

Published 22 Jul 2017 (https://doi.org/10.7490/f1000research.1114460.1)

Author Affiliations

¹ GSK, UK

Copyright © 2017 Ferrero E 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 22 Jul 2017

In silico prediction of novel therapeutic targets using gene disease association data
[version 1; not peer reviewed]

Enrico Ferrero¹, Ian Dunham, Philippe Sanseau

Author Affiliations

¹ GSK, UK

Presented at

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

Abstract

Competing Interests

EF and PS are full-time employees of GSK (GlaxoSmithKline).

Keywords

drug discovery, target discovery, machine learning, neural network

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In silico prediction of novel therapeutic targets using gene disease association data

In silico prediction of novel therapeutic targets using gene disease association data [version 1; not peer reviewed]

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In silico prediction of novel therapeutic targets using gene disease association data
[version 1; not peer reviewed]