PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency.
Saved in:
| Title: | PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency. |
|---|---|
| Authors: | Bernardes-Loch RM; Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa-MG 36570-900, Brazil., de Oliveira Almeida G; Department of Computer Science, Universidade Federal de Viçosa, Viçosa-MG 36570-900, Brazil., Brasiliano IT; Department of Computer Science, Universidade Federal de Viçosa, Viçosa-MG 36570-900, Brazil., Meira W Jr; Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte-MG 31270-901, Brazil., Pires DEV; School of Computing and Information Systems, University of Melbourne, Parkville 3052, Australia., Baracat-Pereira MC; Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa-MG 36570-900, Brazil., de Azevedo Silveira S; Department of Computer Science, Universidade Federal de Viçosa, Viçosa-MG 36570-900, Brazil.; IDATA-Institute of Artificial Intelligence and Computational Science, Universidade Federal de Viçosa, Viçosa-MG 36570-900, Brazil. |
| Source: | Bioinformatics advances [Bioinform Adv] 2025 Sep 08; Vol. 5 (1), pp. vbaf213. Date of Electronic Publication: 2025 Sep 08 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Oxford University Press Country of Publication: England NLM ID: 9918282081306676 Publication Model: eCollection Cited Medium: Internet ISSN: 2635-0041 (Electronic) Linking ISSN: 26350041 NLM ISO Abbreviation: Bioinform Adv Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
|
Full text is not displayed to guests.
Login for full access.
|
|
| ISSN: | 2635-0041 |
|---|---|
| DOI: | 10.1093/bioadv/vbaf213 |
