PCIPG: A comprehensive framework for protein complex identification based on a probabilistic graphical model

Yixiang Huang, Lei Yang, Jiudong Wang, Xinqi Gong

Open source

DOI: 10.1371/journal.pcbi.1014698
Published: 2026-09-03
Container: PLOS Computational Biology
Publisher: Public Library of Science (PLoS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pcbi.1014698,
  title = {PCIPG: A comprehensive framework for protein complex identification based on a probabilistic graphical model},
  author = {Yixiang Huang and Lei Yang and Jiudong Wang and Xinqi Gong},
  year = {2026},
  journal = {PLOS Computational Biology},
  doi = {10.1371/journal.pcbi.1014698},
  url = {https://doi.org/10.1371/journal.pcbi.1014698}
}

RIS

TY  - JOUR
TI  - PCIPG: A comprehensive framework for protein complex identification based on a probabilistic graphical model
AU  - Yixiang Huang
AU  - Lei Yang
AU  - Jiudong Wang
AU  - Xinqi Gong
PY  - 2026
JO  - PLOS Computational Biology
DO  - 10.1371/journal.pcbi.1014698
UR  - https://doi.org/10.1371/journal.pcbi.1014698
ER  -

APA

Huang, Y., Yang, L., Wang, J., & Gong, X. (2026). PCIPG: A comprehensive framework for protein complex identification based on a probabilistic graphical model. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1014698

Source records

crossref · retrieved 2026-11-10T08:07:26.443Z