Phasit Charoenkwan
Chiang Mai University
29 Papers
19 Citations
Phasit Charoenkwan is an academic researcher from Chiang Mai University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 10, co-authored 29 publications.
Chat about Author
Papers
BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides.
Phasit Charoenkwan,Chanin Nantasenamat,Md. Mehedi Hasan,Balachandran Manavalan,Watshara Shoombuatong +4 more
TL;DR: BERT4Bitter as mentioned in this paper is a bidirectional encoder representation from transformers (BERT)-based model for predicting bitter peptides directly from their amino acid sequence without using any structural information.
131
iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides.
TL;DR: The first sequence-based predictor named iUmami-SCM is proposed, which serves as a powerful computational technique for large-scale umami peptide identification as well as facilitating the interpretation ofUmami peptides.
122
iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides
Phasit Charoenkwan,Janchai Yana,Nalini Schaduangrat,Chanin Nantasenamat,Md. Mehedi Hasan,Watshara Shoombuatong +5 more
TL;DR: iBitter-SCM is a computational model that can predict the bitterness of peptides directly from their amino acid sequence without any dependence on their functional domain or structural information and can serve as an important tool to facilitate the high-throughput prediction and de novo design of bitter peptides.
103
iDPPIV-SCM: A Sequence-Based Predictor for Identifying and Analyzing Dipeptidyl Peptidase IV (DPP-IV) Inhibitory Peptides Using a Scoring Card Method.
Phasit Charoenkwan,Sakawrat Kanthawong,Chanin Nantasenamat,Md. Mehedi Hasan,Watshara Shoombuatong +4 more
TL;DR: The proposed iDPPIV-SCM was found to be superior to those of well-known machine learning (ML) classifiers with demonstrated improvements of 2-11%, 4-22% and 7-10% for accuracy, MCC and AUC, respectively, while also achieving comparable results to that of support vector machine.
82
Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method.
Phasit Charoenkwan,Wararat Chiangjong,Vannajan Sanghiran Lee,Chanin Nantasenamat,Md. Mehedi Hasan,Watshara Shoombuatong +5 more
TL;DR: In this paper, a flexible scoring card method (FSCM) was proposed for the development of a sequence-based anticancer peptides (ACP) predictor for improving the prediction accuracy and model interpretability.