Cargando…

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterizatio...

Descripción completa

Detalles Bibliográficos
Autores principales:	Charoenkwan, Phasit, Kanthawong, Sakawrat, Schaduangrat, Nalini, Yana, Janchai, Shoombuatong, Watshara
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2020
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7072630/ https://www.ncbi.nlm.nih.gov/pubmed/32028709 http://dx.doi.org/10.3390/cells9020353

Ejemplares similares

Large-scale comparative review and assessment of computational methods for phage virion proteins identification
por: Kabir, Muhammad, et al.
Publicado: (2022)

SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides
por: Charoenkwan, Phasit, et al.
Publicado: (2022)

iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides
por: Charoenkwan, Phasit, 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)

DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists
por: Schaduangrat, Nalini, et al.
Publicado: (2023)

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)

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties
por: Charoenkwan, Phasit, et al.
Publicado: (2019)

TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus
por: Charoenkwan, Phasit, et al.
Publicado: (2023)

TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides
por: Charoenkwan, Phasit, et al.
Publicado: (2023)

Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins
por: Ahmad, Saeed, et al.
Publicado: (2022)

Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework
por: Charoenkwan, Phasit, et al.
Publicado: (2022)

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)

SCMCRYS: Predicting Protein Crystallization Using an Ensemble Scoring Card Method with Estimating Propensity Scores of P-Collocated Amino Acid Pairs
por: Charoenkwan, Phasit, et al.
Publicado: (2013)

Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins
por: Charoenkwan, Phasit, et al.
Publicado: (2022)

A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

Towards understanding aromatase inhibitory activity via QSAR modeling
por: Shoombuatong, Watshara, et al.
Publicado: (2018)

Unraveling the bioactivity of anticancer peptides as deduced from machine learning
por: Shoombuatong, Watshara, et al.
Publicado: (2018)

Empirical comparison and analysis of machine learning-based approaches for druggable protein identification
por: Shoombuatong, Watshara, et al.
Publicado: (2023)

iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

DeepThal: A Deep Learning-Based Framework for the Large-Scale Prediction of the α(+)-Thalassemia Trait Using Red Blood Cell Parameters
por: Phirom, Krittaya, et al.
Publicado: (2022)

Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation
por: Schaduangrat, Nalini, et al.
Publicado: (2019)

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides
por: Schaduangrat, Nalini, et al.
Publicado: (2019)

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)

SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method
por: Vasylenko, Tamara, et al.
Publicado: (2015)

iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides
por: Laengsri, Vishuda, et al.
Publicado: (2019)

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning
por: Charoenkwan, Phasit, et al.
Publicado: (2022)

Probing the origin of estrogen receptor alpha inhibition via large-scale QSAR study
por: Suvannang, Naravut, et al.
Publicado: (2018)

Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition
por: Huang, Hui-Ling, et al.
Publicado: (2012)

RF_phage virion: Classification of phage virion proteins with a random forest model
por: Zhang, Yanqin, et al.
Publicado: (2023)

Corrigendum: RF_phage virion: classification of phage virion proteins with a random forest model
por: Zhang, Yanqin, et al.
Publicado: (2023)

Prediction of Phage Virion Proteins Using Machine Learning Methods
por: Barman, Ranjan Kumar, et al.
Publicado: (2023)

Learning Perforce SCM
por: Cowham, Robert, et al.
Publicado: (2013)

Evaluation of the SCM test
por: Atkinson, R.J., et al.
Publicado: (1984)

Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods
por: Tan, Jiu-Xin, et al.
Publicado: (2018)

Prediction of aromatase inhibitory activity using the efficient linear method (ELM)
por: Shoombuatong, Watshara, et al.
Publicado: (2015)

SCM: A method to improve network service layout efficiency with network evolution
por: Zhao, Qi, et al.
Publicado: (2017)

Cannot write session to /tmp/vufind_sessions/sess_sdacks5bsscu2oof1nmjf2ddeu