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Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

Representation learning for low-data protein fitness prediction using sequence–structure embeddings and graph neural networks

  • Chanoknun Phunnasorn
  • Narupat Hongdilokkul
  • Wasakorn Laesanklang
  • Charin Modchang
  • Tipaluck Krityakierne
Open at sourcePDFDOICite

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Cite

BibTeXRIS
BibTeX
@article{phunnasorn2026representation,
  title = {{Representation learning for low-data protein fitness prediction using sequence–structure embeddings and graph neural networks}},
  author = {Chanoknun Phunnasorn and Narupat Hongdilokkul and Wasakorn Laesanklang and Charin Modchang and Tipaluck Krityakierne},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-67952-6},
  url = {https://www.nature.com/articles/s41598-026-67952-6},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Representation learning for low-data protein fitness prediction using sequence–structure embeddings and graph neural networks
AU  - Chanoknun Phunnasorn
AU  - Narupat Hongdilokkul
AU  - Wasakorn Laesanklang
AU  - Charin Modchang
AU  - Tipaluck Krityakierne
PY  - 2026
DA  - 2026/10/16
JO  - Scientific Reports
DO  - 10.1038/s41598-026-67952-6
UR  - https://www.nature.com/articles/s41598-026-67952-6
SN  - 2045-2322
ER  -