<p>Variant relevance prediction in extremely imbalanced training sets</p>

Max Schubach; Matteo Re; Peter N. Robinson; Giorgio Valentini

doi:10.7490/f1000research.1114637.1

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Schubach M, Re M, Robinson PN and Valentini G. Variant relevance prediction in extremely imbalanced training sets [version 1; not peer reviewed]. F1000Research 2017, 6(ISCB Comm J):1392 (poster) (https://doi.org/10.7490/f1000research.1114637.1)

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Variant relevance prediction in extremely imbalanced training sets

Max Schubach¹, Matteo Re, Peter N. Robinson, Giorgio Valentini

Published 09 Aug 2017 (https://doi.org/10.7490/f1000research.1114637.1)

Author Affiliations

¹ Berlin Institute of Health (BIH), Germany

Copyright © 2017 Schubach M 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

Variant relevance prediction in extremely imbalanced training sets
[version 1; not peer reviewed]

Max Schubach¹, Matteo Re, Peter N. Robinson, Giorgio Valentini

Author Affiliations

¹ Berlin Institute of Health (BIH), Germany

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

No competing interests were disclosed

Keywords

Regulatory variants, machine-learning, imbalanced datasets, pathogenicity prediction, Mendelian disease, eQTL, ensemble learner, non-coding

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