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A hybrid model combining evolutionary probability and machine learning leverages data-driven protein engineering
View ORCID ProfileAlexander-Maurice Illig, View ORCID ProfileNiklas E. Siedhoff, View ORCID ProfileUlrich Schwaneberg, View ORCID ProfileMehdi D. Davari
doi: https://doi.org/10.1101/2022.06.07.495081
Alexander-Maurice Illig
1Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, Aachen, 52074, North Rhine-Westphalia, Germany
Niklas E. Siedhoff
1Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, Aachen, 52074, North Rhine-Westphalia, Germany
Ulrich Schwaneberg
1Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, Aachen, 52074, North Rhine-Westphalia, Germany
2DWI-Leibniz Institute for Interactive Materials, Forckenbeckstraße 50, Aachen, 52074, North Rhine-Westphalia, Germany
Mehdi D. Davari
3Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, Halle, 06120, Saxony-Anhalt, Germany

- Manuscript SI[supplements/495081_file02.pdf]
Posted June 07, 2022.
A hybrid model combining evolutionary probability and machine learning leverages data-driven protein engineering
Alexander-Maurice Illig, Niklas E. Siedhoff, Ulrich Schwaneberg, Mehdi D. Davari
bioRxiv 2022.06.07.495081; doi: https://doi.org/10.1101/2022.06.07.495081
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