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ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

Molecules (Basel, Switzerland), 2019-05, Vol.24 (10), p.1973

2019

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Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation

International journal of molecular sciences, 2019-11, Vol.20 (22), p.5743

2019

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HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation

Bioinformatics, 2020-06, Vol.36 (11), p.3350-3356

2020

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Towards reproducible computational drug discovery

Journal of cheminformatics, 2020-01, Vol.12 (1), p.9-9, Article 9

2020

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THPep: A machine learning-based approach for predicting tumor homing peptides

Computational biology and chemistry, 2019-06, Vol.80, p.441-451

2019

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StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens

BMC bioinformatics, 2023-07, Vol.24 (1), p.301-301

2023

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Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction

Scientific reports, 2024-02, Vol.14 (1), p.4463-4463

2024

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StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

Scientific reports, 2023-12, Vol.13 (1), p.22994-22994, Article 22994

2023

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TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus

PloS one, 2023-08, Vol.18 (8), p.e0290538-e0290538

2023

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ABCpred: a webserver for the discovery of acetyl- and butyryl-cholinesterase inhibitors

Molecular diversity, 2022-02, Vol.26 (1), p.467-487

2022

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TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides

BMC bioinformatics, 2023-09, Vol.24 (1), p.1-356, Article 356

2023

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Tackling the Antibiotic Resistance Caused by Class A β -Lactamases through the Use of β -Lactamase Inhibitory Protein

International journal of molecular sciences, 2018-07, Vol.19 (8), p.2222

2018

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TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides

International journal of molecular sciences, 2019-06, Vol.20 (12), p.2950

2019

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DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists

Journal of cheminformatics, 2023-05, Vol.15 (1), p.50-50, Article 50

2023

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StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy

Scientific reports, 2022-09, Vol.12 (1), p.16435-16435, Article 16435

2022

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

Cells (Basel, Switzerland), 2020-02, Vol.9 (2), p.353

2020

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ERpred: a web server for the prediction of subtype-specific estrogen receptor antagonists

PeerJ (San Francisco, CA), 2021-07, Vol.9, p.e11716-e11716, Article e11716

2021

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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

International journal of molecular sciences, 2020-04, Vol.21 (7), p.2629

2020

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Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework

iScience, 2022-09, Vol.25 (9), p.104883-104883, Article 104883

2022

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Towards Predicting the Cytochrome P450 Modulation: From QSAR to Proteochemometric Modeling

Current drug metabolism, 2017-07, Vol.18 (6), p.540

2017

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Recent development of machine learning-based methods for the prediction of defensin family and subfamily

EXCLI journal, 2022-01, Vol.21, p.757-771

2022

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CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins

Journal of chemistry, 2017-01, Vol.2017, p.1-15

2017

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Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins

EXCLI journal, 2022-01, Vol.21, p.554-570

2022

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Unraveling the bioactivity of anticancer peptides as deduced from machine learning

EXCLI journal, 2018-01, Vol.17, p.734-752

2018

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iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties

International journal of molecular sciences, 2019-12, Vol.21 (1)

2019

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ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation

HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation

Towards reproducible computational drug discovery

THPep: A machine learning-based approach for predicting tumor homing peptides

StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens

Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus

ABCpred: a webserver for the discovery of acetyl- and butyryl-cholinesterase inhibitors

TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides

Tackling the Antibiotic Resistance Caused by Class A β -Lactamases through the Use of β -Lactamase Inhibitory Protein

TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides

DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists

StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy

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

ERpred: a web server for the prediction of subtype-specific estrogen receptor antagonists

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

Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework

Towards Predicting the Cytochrome P450 Modulation: From QSAR to Proteochemometric Modeling

Recent development of machine learning-based methods for the prediction of defensin family and subfamily

CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins

Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins

Unraveling the bioactivity of anticancer peptides as deduced from machine learning

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties

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