KEGG orthology prediction of bacterial proteins using natural language processing.

Journal: BMC bioinformatics
PMID: 38600441

Abstract

BACKGROUND: The advent of high-throughput technologies has led to an exponential increase in uncharacterized bacterial protein sequences, surpassing the capacity of manual curation. A large number of bacterial protein sequences remain unannotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology, making it necessary to use auto annotation tools. These tools are now indispensable in the biological research landscape, bridging the gap between the vastness of unannotated sequences and meaningful biological insights.

Authors

  • Jing Chen
    Department of Vascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
  • Haoyu Wu
    School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.
  • Ning Wang
    Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, Shandong, China.

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