Authors
Zhaoxi Yu, Lingjie Meng, Canh Hao Nguyen, Hiroshi Mamitsuka, Minoru Kanehisa, Hiroyuki Ogata
Published in
Briefings in bioinformatics. Volume 27. Issue 4. Jul 03, 2026.
Abstract
The KEGG Orthology (KO) system links DNA and protein sequences to biological functions and pathways, providing a curated, fundamental, and consistent annotation framework across all domains of life. While accurate, traditional sequence alignment-based annotation methods are computationally expensive, which severely limits their application in large-scale datasets. To address this challenge, we introduce Deep KEGG Orthology and Links Annotation (DeepKOALA), a deep learning approach based on Gated Recurrent Units (GRU), which frames KO annotation as an open-set recognition task. This design reduces false positives arising from out-of-scope sequences and, together with a lightweight GRU backbone, enables high-throughput annotation. The GRU-based model was benchmarked against four other deep learning architectures and showed the best balance between speed and accuracy. We then trained a GRU-based model, DeepKOALA, and performed a cross-species evaluation against existing KO annotation tools. In this comparison, DeepKOALA achieved a F1 of 83.37%, which is comparable to existing alignment-based tools. Meanwhile, the speed of DeepKOALA was 36.5-fold faster than Blast KEGG Orthology and Links Annotation (BlastKOALA). We also provide a specialized fragment model for handling incomplete sequences and an optional multi-domain mode. Together, these features make DeepKOALA a scalable and efficient option for high-throughput function annotation.
PMID:
42617151
Bibliographic data and abstract were imported from PubMed on 20 Aug 2026.
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