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Clustering FunFams using sequence embeddings improves EC purity
View ORCID ProfileMaria Littmann, View ORCID ProfileNicola Bordin, View ORCID ProfileMichael Heinzinger, View ORCID ProfileChristine Orengo, View ORCID ProfileBurkhard Rost
doi: https://doi.org/10.1101/2021.01.21.427551
Maria Littmann
1TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
2TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
Nicola Bordin
3Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
Michael Heinzinger
1TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
2TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
Christine Orengo
3Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
Burkhard Rost
1TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
4Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
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Posted January 21, 2021.
Clustering FunFams using sequence embeddings improves EC purity
Maria Littmann, Nicola Bordin, Michael Heinzinger, Christine Orengo, Burkhard Rost
bioRxiv 2021.01.21.427551; doi: https://doi.org/10.1101/2021.01.21.427551
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