Cargando…
iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities
Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens’ increasing resistance to antibiotics, AMPs have the potential to...
Autores principales: | Xu, Jing, Li, Fuyi, Li, Chen, Guo, Xudong, Landersdorfer, Cornelia, Shen, Hsin-Hui, Peleg, Anton Y, Li, Jian, Imoto, Seiya, Yao, Jianhua, Akutsu, Tatsuya, Song, Jiangning |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359087/ https://www.ncbi.nlm.nih.gov/pubmed/37369638 http://dx.doi.org/10.1093/bib/bbad240 |
Ejemplares similares
-
ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning
por: Jia, Xiaoti, et al.
Publicado: (2023) -
ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species
por: Chen, Ruyi, et al.
Publicado: (2023) -
ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning
por: Wang, Xiaoyu, et al.
Publicado: (2022) -
RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins
por: Peng, Xinxin, et al.
Publicado: (2022) -
SMG: self-supervised masked graph learning for cancer gene identification
por: Cui, Yan, et al.
Publicado: (2023)