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TMbed: transmembrane proteins predicted through language model embeddings
Michael Bernhofer,
Burkhard Rost
Informatics 12 - Chair of Bioinformatics
Technical University of Munich
Research output
:
Contribution to journal
›
Article
›
peer-review
22
Scopus citations
Overview
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Keyphrases
3D Structure
100%
Language Model
100%
Transmembrane Protein
100%
Model Embedding
100%
False Positive Rate
66%
Desktop Computer
66%
β-barrel
66%
AlphaFold
66%
Protein Language Model
66%
Protein Level
33%
Novel Filter
33%
Graphics Card
33%
NVIDIA
33%
Performance Level
33%
Molecular Biology
33%
Proteome
33%
Transmembrane
33%
Signal Peptide
33%
Protein Family
33%
Molecular Medicine
33%
Proteome-wide
33%
Redundant Data
33%
Viterbi
33%
Transmembrane Segment
33%
α-helical
33%
Consumer-grade
33%
Multiple Sequence Alignment
33%
Transmembrane Helix
33%
Transmembrane Region
33%
Evolutionary Information
33%
Non-membrane Proteins
33%
α-helical Barrel
33%
Segment-level
33%
Biochemistry, Genetics and Molecular Biology
Beta Barrel
100%
Proteome
100%
Gaussian Distribution
50%
Molecular Biology
50%
Molecular Medicine
50%
Protein Family
50%
Sequence Alignment
50%
Transmembrane Protein
50%
Signal Peptide
50%