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Learning the high-dimensional immunogenomic features that predict public and private antibody repertoires
View ORCID ProfileVictor Greiff, Cédric R. Weber, Johannes Palme, Ulrich Bodenhofer, Enkelejda Miho, Ulrike Menzel, Sai T. Reddy
doi: https://doi.org/10.1101/127902
Victor Greiff
1 Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
Cédric R. Weber
1 Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
Johannes Palme
2 Institute of Bioinformatics, Johannes Kepler University, Linz, Austria
3 Health & Environment Department, Molecular Diagnostics, AIT – Austrian Institute of Technology, Vienna, Austria
Ulrich Bodenhofer
2 Institute of Bioinformatics, Johannes Kepler University, Linz, Austria
Enkelejda Miho
1 Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
Ulrike Menzel
1 Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
Sai T. Reddy
1 Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
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Posted April 18, 2017.
Learning the high-dimensional immunogenomic features that predict public and private antibody repertoires
Victor Greiff, Cédric R. Weber, Johannes Palme, Ulrich Bodenhofer, Enkelejda Miho, Ulrike Menzel, Sai T. Reddy
bioRxiv 127902; doi: https://doi.org/10.1101/127902
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