<p>Machine learning tools to computationally identify genomic elements </p>

Melissa Woghiren; Yifeng Li; Alioune Ngom

doi:10.7490/f1000research.1113373.1

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Woghiren M, Li Y and Ngom A. Machine learning tools to computationally identify genomic elements [version 1; not peer reviewed]. F1000Research 2016, 5(ISCB Comm J):2636 (poster) (https://doi.org/10.7490/f1000research.1113373.1)

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Machine learning tools to computationally identify genomic elements

Melissa Woghiren¹, Yifeng Li, Alioune Ngom

Published 04 Nov 2016 (https://doi.org/10.7490/f1000research.1113373.1)

Author Affiliations

¹ University of Alberta, Canada

Copyright © 2016 Woghiren 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 04 Nov 2016

Machine learning tools to computationally identify genomic elements
[version 1; not peer reviewed]

Melissa Woghiren¹, Yifeng Li, Alioune Ngom

Author Affiliations

¹ University of Alberta, Canada

Presented at

4th International Society for Computational Biology Latin America Bioinformatics Conference 2016

Abstract

Competing Interests

No competing interests were disclosed

Keywords

non-coding regions, cis-regulatory elements, promoter, enhancer, machine learning, random forest, supervised learning

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