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Clustering FunFams using sequence embeddings improves EC purity
Maria Littmann
, Nicola Bordin
, Michael Heinzinger
, Konstantin Schütze
, Christian Dallago
, Christine Orengo
,
Burkhard Rost
Informatics 12 - Chair of Bioinformatics
Technical University of Munich
University College London
Research output
:
Contribution to journal
›
Article
›
peer-review
18
Scopus citations
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Dive into the research topics of 'Clustering FunFams using sequence embeddings improves EC purity'. Together they form a unique fingerprint.
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Keyphrases
Functional Family
100%
Sequence Embedding
100%
Cluster Functional
22%
Knowledge Transfer
11%
Protein Function
11%
Amino Acids
11%
Functional Annotation
11%
Density-based Spatial Clustering of Applications with Noise (DBSCAN)
11%
Language Model
11%
Random Clustering
11%
Family Cluster
11%
Model Transfer
11%
Generating Functional
11%
Family Needs
11%
Functional Consistency
11%
Shared Function
11%
Biochemistry, Genetics and Molecular Biology
Class Architecture Topology Homology
100%
Amino Acids
50%
Protein Function
50%