StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists by Nalini Schaduangrat, Nutta Homdee, Watshara Shoombuatong

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StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens by Phasit Charoenkwan, Nalini Schaduangrat, Watshara Shoombuatong

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ERpred: a web server for the prediction of subtype-specific estrogen receptor antagonists by Nalini Schaduangrat, Aijaz Ahmad Malik, Chanin Nantasenamat

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DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists by Nalini Schaduangrat, Nuttapat Anuwongcharoen, Phasit Charoenkwan, Watshara Shoombuatong

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Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction by Watshara Shoombuatong, Nutta Homdee, Nalini Schaduangrat, Pramote Chumnanpuen

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ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides by Nalini Schaduangrat, Chanin Nantasenamat, Virapong Prachayasittikul, Watshara Shoombuatong

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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... by Phasit Charoenkwan, Nalini Schaduangrat, Chanin Nantasenamat, Theeraphon Piacham, Watshara Shoombuatong

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TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides by Phasit Charoenkwan, Sasikarn Kongsompong, Nalini Schaduangrat, Pramote Chumnanpuen, Watshara Shoombuatong

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TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus. by Phasit Charoenkwan, Sajee Waramit, Pramote Chumnanpuen, Nalini Schaduangrat, Watshara Shoombuatong

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PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method by Phasit Charoenkwan, Sakawrat Kanthawong, Nalini Schaduangrat, Janchai Yana, Watshara Shoombuatong

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Recent development of machine learning-based methods for the prediction of defensin family and subfamily by Phasit Charoenkwan, Nalini Schaduangrat, S. M. Hasan Mahmud, Orawit Thinnukool, Watshara Shoombuatong

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Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework by Phasit Charoenkwan, Nalini Schaduangrat, Pietro Lio’, Mohammad Ali Moni, Watshara Shoombuatong, Balachandran Manavalan

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StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy by Nalini Schaduangrat, Nuttapat Anuwongcharoen, Mohammad Ali Moni, Pietro Lio’, Phasit Charoenkwan, Watshara Shoombuatong

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Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins by Phasit Charoenkwan, Nalini Schaduangrat, Md Mehedi Hasan, Mohammad Ali Moni, Pietro Lió, Watshara Shoombuatong

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