PyPropel: a Python-based tool for efficiently processing and characterising protein data
PyPropel provides added value over existing tools by offering a unified workflow that encompasses the full spectrum of protein research, from raw data pre-processing to functional annotation and model performance analysis, thereby supporting efficient protein function studies.
Authors
- Jinlong Ru (ORCID: https://orcid.org/0000-0002-6757-6018)
- Adam P. Cribbs (ORCID: https://orcid.org/0000-0001-5288-3077)
- Jianfeng Sun (ORCID: https://orcid.org/0000-0002-1274-5080)
- Dapeng Xiong (ORCID: https://orcid.org/0000-0002-8381-5680)
Institutions
- Cornell University (US)
- University of Oxford (GB)
- Technical University of Munich (DE)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1186/s12859-025-06079-3
- Citations
- 2
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 1.61