Functional annotation of enzyme-encoding genes using deep learning with transformer layers.
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ABSTRACT:
SUBMITTER: Kim GB
PROVIDER: S-EPMC10645960 | biostudies-literature | 2023 Nov
REPOSITORIES: biostudies-literature
Publications
Functional annotation of enzyme-encoding genes using deep learning with transformer layers.
Kim Gi Bae GB Kim Ji Yeon JY Lee Jong An JA Norsigian Charles J CJ Palsson Bernhard O BO Lee Sang Yup SY
Nature communications 20231114 1
Functional annotation of open reading frames in microbial genomes remains substantially incomplete. Enzymes constitute the most prevalent functional gene class in microbial genomes and can be described by their specific catalytic functions using the Enzyme Commission (EC) number. Consequently, the ability to predict EC numbers could substantially reduce the number of un-annotated genes. Here we present a deep learning model, DeepECtransformer, which utilizes transformer layers as a neural networ ...[more]
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