<p>Distill, a random forest, xgboost, and deep neural network based ensemble learner, provides class leading DNA variant pathogenicity prediction</p>

David McGaughey

doi:10.7490/f1000research.1116045.1

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McGaughey D. Distill, a random forest, xgboost, and deep neural network based ensemble learner, provides class leading DNA variant pathogenicity prediction [version 1; not peer reviewed]. F1000Research 2018, 7:1413 (poster) (https://doi.org/10.7490/f1000research.1116045.1)

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Distill, a random forest, xgboost, and deep neural network based ensemble learner, provides class leading DNA variant pathogenicity prediction

David McGaughey¹

Published 05 Sep 2018 (https://doi.org/10.7490/f1000research.1116045.1)

Author Affiliations

¹ National Institutes of Health, USA

Copyright © 2018 McGaughey D. 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. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions.

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CITE

Published 05 Sep 2018

Distill, a random forest, xgboost, and deep neural network based ensemble learner, provides class leading DNA variant pathogenicity prediction
[version 1; not peer reviewed]

David McGaughey¹

Author Affiliations

¹ National Institutes of Health, USA

Presented at

Genome Informatics 2018

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Pathogenicity, DNA, machine learning, random forest, xgboost, retina, WGS

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