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Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

Integrating disulfidptosis patterns and machine learning identifies a novel diagnostic signature for endometriosis

  • Qingqing Li
  • Boen Zhao
  • Yulin Yuan
  • Lichuan Lai
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Cite

BibTeXRIS
BibTeX
@article{li2026integrating,
  title = {{Integrating disulfidptosis patterns and machine learning identifies a novel diagnostic signature for endometriosis}},
  author = {Qingqing Li and Boen Zhao and Yulin Yuan and Lichuan Lai},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-76837-7},
  url = {https://www.nature.com/articles/s41598-026-76837-7},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Integrating disulfidptosis patterns and machine learning identifies a novel diagnostic signature for endometriosis
AU  - Qingqing Li
AU  - Boen Zhao
AU  - Yulin Yuan
AU  - Lichuan Lai
PY  - 2026
DA  - 2026/10/20
JO  - Scientific Reports
DO  - 10.1038/s41598-026-76837-7
UR  - https://www.nature.com/articles/s41598-026-76837-7
SN  - 2045-2322
ER  -