<p>Predicting disease progression for ALS clinic patients</p>

Albert A.  Taylor; Christina Fournier; Meraida Polak; Liuxia Wang; Neta Zach; Joell Shepperson; John Reichert; Mike Keymer; Jonathan D.  Glass; David L.  Ennist

doi:10.7490/f1000research.1113586.1

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Taylor AA, Fournier C, Polak M et al. Predicting disease progression for ALS clinic patients [version 1; not peer reviewed]. F1000Research 2017, 6:4 (poster) (https://doi.org/10.7490/f1000research.1113586.1)

NOTE: it is important to ensure the information in square brackets after the title is included in this citation.

Predicting disease progression for ALS clinic patients

Albert A. Taylor, Christina Fournier, Meraida Polak, Liuxia Wang, Neta Zach, Joell Shepperson, John Reichert, Mike Keymer, Jonathan D. Glass, David L. Ennist¹

Published 03 Jan 2017 (https://doi.org/10.7490/f1000research.1113586.1)

Author Affiliations

¹ Origent Data Sciences, Inc., USA

Copyright © 2017 Taylor AA 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 03 Jan 2017

Predicting disease progression for ALS clinic patients
[version 1; not peer reviewed]

Albert A. Taylor, Christina Fournier, Meraida Polak, Liuxia Wang, Neta Zach, Joell Shepperson, John Reichert, Mike Keymer, Jonathan D. Glass, David L. Ennist¹

Author Affiliations

¹ Origent Data Sciences, Inc., USA

Presented at

27th International Symposium on ALS/MND 2016

Abstract

Competing Interests

No competing interests were disclosed

Keywords

ALS, predicting disease progression, machine learning, ALSFRS-R

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