<p>A discriminatory method for the identification of key transcriptional regulators using epigenetic data</p>

Florian Schmidt; Tzung-Chien Hsieh; Nina Gasparoni; Gilles Gasparoni; Julia K. Polansky; Oliver Gorka; Jürgen Roland; Karl Nordström; Anupam Sinha; Wei Chen; Alf Hamann; Philip Rosenstiel; Jörn Walter; Marcel H. Schulz

doi:10.7490/f1000research.1114542.1

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Schmidt F, Hsieh TC, Gasparoni N et al. A discriminatory method for the identification of key transcriptional regulators using epigenetic data [version 1; not peer reviewed]. F1000Research 2017, 6(ISCB Comm J):1284 (poster) (https://doi.org/10.7490/f1000research.1114542.1)

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A discriminatory method for the identification of key transcriptional regulators using epigenetic data

Florian Schmidt¹, Tzung-Chien Hsieh, Nina Gasparoni, Gilles Gasparoni, Julia K. Polansky, Oliver Gorka, Jürgen Roland, Karl Nordström, Anupam Sinha, Wei Chen, Alf Hamann, Philip Rosenstiel, Jörn Walter, Marcel H. Schulz

Published 31 Jul 2017 (https://doi.org/10.7490/f1000research.1114542.1)

Author Affiliations

¹ Saarland University, Germany

Copyright © 2017 Schmidt F 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 31 Jul 2017

A discriminatory method for the identification of key transcriptional regulators using epigenetic data
[version 1; not peer reviewed]

Author Affiliations

¹ Saarland University, 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

Transcription factors, epigenetics, transcriptional regulation, machine learning

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