Cargando…
ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation
Anticancer peptides (ACPs) have provided a promising perspective for cancer treatment, and the prediction of ACPs is very important for the discovery of new cancer treatment drugs. It is time consuming and expensive to use experimental methods to identify ACPs, so computational methods for ACP ident...
Autores principales: | Chen, Xian-gan, Zhang, Wen, Yang, Xiaofei, Li, Chenhong, Chen, Hengling |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279753/ https://www.ncbi.nlm.nih.gov/pubmed/34276801 http://dx.doi.org/10.3389/fgene.2021.698477 |
Ejemplares similares
-
ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree
por: Li, Yanjuan, et al.
Publicado: (2023) -
ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides
por: Bhattarai, Sadik, et al.
Publicado: (2022) -
EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides
por: Ge, Ruiquan, et al.
Publicado: (2020) -
iACP: a sequence-based tool for identifying anticancer peptides
por: Chen, Wei, et al.
Publicado: (2016) -
ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides
por: Ahmed, Sajid, et al.
Publicado: (2021)