Phasit Charoenkwan

iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides

Phasit Charoenkwan, Janchai Yana, Chanin Nantasenamat et al. · Journal of Chemical Information and Modeling · 2020 · 185 citations

Agriculture, Bioinformatics, Food Science +12

BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides

Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan et al. · Bioinformatics · 2021 · 176 citations

Life Sciences, Biochemistry, Bioinformatics +11

NeurphologyJ: An automatic neuronal morphology quantification method and its application in pharmacological discovery

Shinn‐Ying Ho, Chih-Yuan Chao, Hui-Ling Huang et al. · BMC Bioinformatics · 2011 · 165 citations · Full text

Life Sciences, Neurobiology, Neuroscience +15

StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides

Phasit Charoenkwan, Wararat Chiangjong, Chanin Nantasenamat et al. · Briefings in Bioinformatics · 2021 · 137 citations

Life Sciences, Immunology, Immunotherapy +16

iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides

Phasit Charoenkwan, Janchai Yana, Nalini Schaduangrat et al. · Genomics · 2020 · 133 citations

Pharmacy, Medicinal Chemistry, Pharmaceutical Analysis +10