<p>Lipoinformatics – machine learning approach to study lipid profiles</p>

Neetika Nath; Christian  Klose; Mathias  Gerl; Michal A.  Surma; Kai  Simons; Lars  Kaderali

doi:10.7490/f1000research.1112979.1

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Nath N, Klose C, Gerl M et al. Lipoinformatics – machine learning approach to study lipid profiles [version 1; not peer reviewed]. F1000Research 2016, 5:2144 (poster) (https://doi.org/10.7490/f1000research.1112979.1)

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Lipoinformatics – machine learning approach to study lipid profiles

Neetika Nath¹, Christian Klose, Mathias Gerl, Michal A. Surma, Kai Simons, Lars Kaderali

Published 01 Sep 2016 (https://doi.org/10.7490/f1000research.1112979.1)

Author Affiliations

¹ University Medicine Greifswald, Germany

Copyright © 2016 Nath N 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 01 Sep 2016

Lipoinformatics – machine learning approach to study lipid profiles
[version 1; not peer reviewed]

Neetika Nath¹, Christian Klose, Mathias Gerl, Michal A. Surma, Kai Simons, Lars Kaderali

Author Affiliations

¹ University Medicine Greifswald, Germany

Presented at

10th International Workshop on Machine Learning in Systems Biology (MLSB) 2016

15th European Conference on Computational Biology (ECCB) 2016

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Lipidomics, machine learning, BMI

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