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Prediction of enzyme function using an interpretable optimized ensemble learning framework

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Dhibar, Saikat, Basak, Sumon, Jana, Biman·2025·Chemical Science
DOI 10.1039/d5sc04513dPMC PMC12424445

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Abstract

Accurate prediction of enzyme function, particularly for newly discovered uncharacterized sequences, is immensely important for modern biological research.

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Metadata

Authors
Dhibar, S., Basak, S., & Jana, B.
Affiliations
Indian Association for the Cultivation of Science (IN)
Corresponding author
Jana, Biman
ORCID
Jana, Biman
Venue
Chemical Science
Publisher
Royal Society of Chemistry (RSC)
Published
2025
Volume
16
Issue
39
Pages
18438-18449
ISSN
  • 2041-6520 (ISSN-L)
  • 2041-6539
DOI
10.1039/d5sc04513d
PMC
PMC12424445
OpenAlex
W4413893409
Cited by
5
References
4
Citations by year
2026: 4 · 2025: 1
FWCI
1.50
Type
journal article
Language
en
Open access
Licence
Locations
  • Chemical Science (publishedVersion, cc-by-nc)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc)
Funders
  • Indian Association for the Cultivation of Science
  • Science and Engineering Research Board (CRG/2020/000756)
  • Department of Science and Technology, Ministry of Science and Technology, India
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
enzyme function prediction, enzyme commission numbers, protein primary sequence, model interpretability, ensemble learning, weighted voting, focal loss, class imbalance, Shapley value, functional motifs, catalytic site, allosteric site, hypothetical protein, SOLVE

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