ME-PFP: An Ensemble Learning Approach Fusing Multi-Source Features for Protein Function Prediction.

Luo H, Hu Y, Song C, Li X, Ma Y, Qian Y, Deng L

Open source

DOI
10.1021/acs.jcim.5c02513
Published
2026 Feb 23
Container
Journal of chemical information and modeling
Publisher
Not recorded
Open access
unknown

Credibility signals

limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.1021/acs.jcim.5c02513,
  title = {ME-PFP: An Ensemble Learning Approach Fusing Multi-Source Features for Protein Function Prediction.},
  author = {Luo H and Hu Y and Song C and Li X and Ma Y and Qian Y and Deng L},
  year = {2026},
  journal = {Journal of chemical information and modeling},
  doi = {10.1021/acs.jcim.5c02513},
  url = {https://doi.org/10.1021/acs.jcim.5c02513}
}

RIS

TY  - JOUR
TI  - ME-PFP: An Ensemble Learning Approach Fusing Multi-Source Features for Protein Function Prediction.
AU  - Luo H
AU  - Hu Y
AU  - Song C
AU  - Li X
AU  - Ma Y
AU  - Qian Y
AU  - Deng L
PY  - 2026
JO  - Journal of chemical information and modeling
DO  - 10.1021/acs.jcim.5c02513
UR  - https://doi.org/10.1021/acs.jcim.5c02513
ER  - 

APA

H, L., Y, H., C, S., X, L., Y, M., Y, Q., & L, D. (2026). ME-PFP: An Ensemble Learning Approach Fusing Multi-Source Features for Protein Function Prediction.. Journal of chemical information and modeling. https://doi.org/10.1021/acs.jcim.5c02513

Source records