Cargando…
iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features
Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale pro...
Autores principales: | Charoenkwan, Phasit, Nantasenamat, Chanin, Hasan, Md. Mehedi, Moni, Mohammad Ali, Lio’, Pietro, Shoombuatong, Watshara |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8396555/ https://www.ncbi.nlm.nih.gov/pubmed/34445663 http://dx.doi.org/10.3390/ijms22168958 |
Ejemplares similares
-
UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning
por: Charoenkwan, Phasit, et al.
Publicado: (2021) -
SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids
por: Charoenkwan, Phasit, et al.
Publicado: (2022) -
Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method
por: Charoenkwan, Phasit, et al.
Publicado: (2021) -
Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins
por: Charoenkwan, Phasit, et al.
Publicado: (2022) -
AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning
por: Charoenkwan, Phasit, et al.
Publicado: (2022)