<p>Application of the random forest method in identification of candidate genes in quantitative trait loci regions for adaptive immune responses of chicken</p>

Aneta Polewko-Klim; Wojciech Lesiński; Agnieszka Kitlas Golińska; Maria Siwek; Krzysztof Mnich; Witold Rudnicki

doi:10.7490/f1000research.1113018.1

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Polewko-Klim A, Lesiński W, Kitlas Golińska A et al. Application of the random forest method in identification of candidate genes in quantitative trait loci regions for adaptive immune responses of chicken [version 1; not peer reviewed]. F1000Research 2016, 5:2184 (poster) (https://doi.org/10.7490/f1000research.1113018.1)

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Application of the random forest method in identification of candidate genes in quantitative trait loci regions for adaptive immune responses of chicken

Aneta Polewko-Klim¹, Wojciech Lesiński ¹, Agnieszka Kitlas Golińska¹, Maria Siwek², Krzysztof Mnich¹, Witold Rudnicki³

Published 02 Sep 2016 (https://doi.org/10.7490/f1000research.1113018.1)

Author Affiliations

¹ University of Białystok, Poland
² University of Technology and Life Sciences, Poland
³ University of Białystok , University of Warsaw, Poland

Copyright © 2016 Polewko-Klim A 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 02 Sep 2016

Application of the random forest method in identification of candidate genes in quantitative trait loci regions for adaptive immune responses of chicken
[version 1; not peer reviewed]

Aneta Polewko-Klim¹, Wojciech Lesiński ¹, Agnieszka Kitlas Golińska¹, Maria Siwek², Krzysztof Mnich¹, Witold Rudnicki³

Author Affiliations

¹ University of Białystok, Poland
² University of Technology and Life Sciences, Poland
³ University of Białystok , University of Warsaw, Poland

Presented at

15th European Conference on Computational Biology (ECCB) 2016

Abstract

Competing Interests

No competing interests were disclosed

Keywords

SNPs, genetic markers, feature selection, Random Forest, Boruta algorithm

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