AutoPeptideML: a study on how to build more trustworthy peptide bioactivity predictors
Source code, documentation, and data are available at https://github.com/IBM/AutoPeptideML and a dedicated web-server at http://peptide.ucd.ie/AutoPeptideML. A static version of the software to ensure the reproduction of the results is available at https://zenodo.org/records/13363975.
Authors
- Denis C. Shields (ORCID: https://orcid.org/0000-0003-4015-2474)
- Hoang Thanh Lam
- Vanessa López (ORCID: https://orcid.org/0000-0002-0674-5324)
- Rodrigo Cossio-Pérez (ORCID: https://orcid.org/0000-0002-4407-7179)
- Clement Agoni (ORCID: https://orcid.org/0000-0001-6130-8031)
- Raúl Fernández-Díaz (ORCID: https://orcid.org/0000-0002-7383-6568)
Institutions
- University College Dublin (IE)
- National University of Quilmes (AR)
- IBM (Ireland) (IE)
- Synthetic Genomics (United States) (US)
- IBM Research - Ireland (IE)
- University of KwaZulu-Natal (ZA)
Publication Details
- Journal
- Bioinformatics
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1093/bioinformatics/btae555
- Citations
- 9
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 2.10
Funders
- European Commission
- Enterprise Ireland
- Science Foundation Ireland