Cargando…
DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists
Drug resistance represents a major obstacle to therapeutic innovations and is a prevalent feature in prostate cancer (PCa). Androgen receptors (ARs) are the hallmark therapeutic target for prostate cancer modulation and AR antagonists have achieved great success. However, rapid emergence of resistan...
Autores principales: | Schaduangrat, Nalini, Anuwongcharoen, Nuttapat, Charoenkwan, Phasit, Shoombuatong, Watshara |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163717/ https://www.ncbi.nlm.nih.gov/pubmed/37149650 http://dx.doi.org/10.1186/s13321-023-00721-z |
Ejemplares similares
-
StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy
por: Schaduangrat, Nalini, et al.
Publicado: (2022) -
StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens
por: Charoenkwan, Phasit, et al.
Publicado: (2023) -
Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework
por: Charoenkwan, Phasit, et al.
Publicado: (2022) -
Recent development of machine learning-based methods for the prediction of defensin family and subfamily
por: Charoenkwan, Phasit, et al.
Publicado: (2022) -
Correction: Shoombuatong, W., et al. iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties. Int. J. Mol. Sci. 2020, 21, 75
por: Charoenkwan, Phasit, et al.
Publicado: (2020)