Cargando…
EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides
As cancer remains one of the main threats of human life, developing efficient cancer treatments is urgent. Anticancer peptides, which could overcome the significant side effects and poor results of traditional cancer treatments, have become a new potential alternative these years. However, identifyi...
Autores principales: | Ge, Ruiquan, Feng, Guanwen, Jing, Xiaoyang, Zhang, Renfeng, Wang, Pu, Wu, Qing |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438906/ https://www.ncbi.nlm.nih.gov/pubmed/32903636 http://dx.doi.org/10.3389/fgene.2020.00760 |
Ejemplares similares
-
i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification
por: Jiang, Minchao, et al.
Publicado: (2022) -
ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree
por: Li, Yanjuan, et al.
Publicado: (2023) -
ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation
por: Chen, Xian-gan, et al.
Publicado: (2021) -
Editorial: Machine learning for peptide structure, function, and design
por: Ge, Ruiquan, et al.
Publicado: (2022) -
DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm
por: Yu, Lezheng, et al.
Publicado: (2020)