<p>Predicting non-coding variant impact on the human brain: a machine learning approach</p>

Kevin Vervier; Abdollah Dehzangi; Jacob Michaelson

doi:10.7490/f1000research.1112677.1

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Vervier K, Dehzangi A and Michaelson J. Predicting non-coding variant impact on the human brain: a machine learning approach [version 1; not peer reviewed]. F1000Research 2016, 5(ISCB Comm J):1790 (poster) (https://doi.org/10.7490/f1000research.1112677.1)

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Predicting non-coding variant impact on the human brain: a machine learning approach

Kevin Vervier¹, Abdollah Dehzangi, Jacob Michaelson

Published 22 Jul 2016 (https://doi.org/10.7490/f1000research.1112677.1)

Author Affiliations

¹ University of Iowa Hospitals and Clinics, USA

Copyright © 2016 Vervier K 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 2016

Predicting non-coding variant impact on the human brain: a machine learning approach
[version 1; not peer reviewed]

Kevin Vervier¹, Abdollah Dehzangi, Jacob Michaelson

Author Affiliations

¹ University of Iowa Hospitals and Clinics, USA

Presented at

International Conference on Intelligent Systems for Molecular Biology (ISMB) 2016

Abstract

Competing Interests

No competing interests were disclosed

Keywords

variant annotation, machine learning, data integration, human brain, tissue-specific

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