841209
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10.5281/zenodo.841209
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Wu, Yibo
ETH Zurich, Institute of Molecular Systems Biology
Williams, Evan G.
ETH Zurich, Institute of Molecular Systems Biology
Rodriguez Martinez, Maria
IBM Zurich Research Laboratory
Aebersold, Ruedi
ETH Zurich, Institute of Molecular Systems Biology / Faculty of Science, University of Zurich
Selection of stable biomarker signature for prediction of metabolic phenotypes
Cuklina, Jelena
ETH Zurich, Institute of Molecular Systems Biology / Ph.D. Program in Systems Biology, University of Zurich and ETH Zurich / IBM Zurich Research Laboratory
info:eu-repo/semantics/openAccess
Creative Commons Attribution Non Commercial No Derivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
<p>In biomarker research, the goal is to construct an prediction rule on the basis of a small number of predictors. Formally, this means representing a macro-level response as a function of molecular features (DNA variants, transcript or protein abundancies) with minimal error.</p>
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<p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 668858. This work was supported (in part) by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0324-2. The opinions expressed and arguments employed therein do not necessarily reflect the official views of the Swiss Government.</strong></p>
<p> </p>
The information in this document is provided as is, and no guarantee or warranty is given that the information is fit for any particular purpose. The content of this document reflects only the author's view - the European Commission is not responsible for any use that may be made of the information it contains. The users use the information at their sole risk and liability.
Zenodo
2017-07-24
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841208
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award_title=PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION; award_number=668858; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/668858; funder_id=00k4n6c32; funder_name=European Commission;
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