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ORCIDPhasit Charoenkwan
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2020 – today
- 2025
[j27]Phasit Charoenkwan, Nalini Schaduangrat, Le Thi Phan, Balachandran Manavalan
, Watshara Shoombuatong:
M3S-ALG: Improved and robust prediction of allergenicity of chemical compounds by using a novel multi-step stacking strategy. Future Gener. Comput. Syst. 162: 107455 (2025)- 2024
- [j26]Thanakorn Chaisen, Phasit Charoenkwan, Cheong Ghil Kim, Pree Thiengburanathum:
A Zero-Shot Interpretable Framework for Sentiment Polarity Extraction. IEEE Access 12: 10586-10607 (2024) - 2023
- [j25]Pree Thiengburanathum, Phasit Charoenkwan:
SETAR: Stacking Ensemble Learning for Thai Sentiment Analysis Using RoBERTa and Hybrid Feature Representation. IEEE Access 11: 92822-92837 (2023) - [j24]Phasit Charoenkwan, Nalini Schaduangrat, Watshara Shoombuatong:
StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens. BMC Bioinform. 24(1): 301 (2023) - [j23]Phasit Charoenkwan, Sasikarn Kongsompong, Nalini Schaduangrat, Pramote Chumnanpuen, Watshara Shoombuatong:
TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides. BMC Bioinform. 24(1): 356 (2023) - [j22]Phasit Charoenkwan, Chonlatip Pipattanaboon, Chanin Nantasenamat, Md. Mehedi Hasan, Mohammad Ali Moni, Pietro Lio', Watshara Shoombuatong:
PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning. Comput. Biol. Medicine 152: 106368 (2023) - [j21]Phasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, Changmin Oh, Balachandran Manavalan, Watshara Shoombuatong:
PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning. Comput. Biol. Medicine 158: 106784 (2023) - [j20]Nalini Schaduangrat, Nuttapat Anuwongcharoen, Phasit Charoenkwan, Watshara Shoombuatong:
DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists. J. Cheminformatics 15(1): 50 (2023) - 2022
- [j19]Ratapol Wudhikarn, Phasit Charoenkwan, Kanokwan Malang:
Deep Learning in Barcode Recognition: A Systematic Literature Review. IEEE Access 10: 8049-8072 (2022) - [j18]Phasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, Pietro Lio', Balachandran Manavalan, Watshara Shoombuatong:
SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins. Comput. Biol. Medicine 146: 105704 (2022) - [j17]Phasit Charoenkwan, Nalini Schaduangrat, Pietro Lio', Mohammad Ali Moni, Balachandran Manavalan, Watshara Shoombuatong:
NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides. Comput. Biol. Medicine 148: 105700 (2022) - [j16]Phasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, Pietro Lio', Mohammad Ali Moni, Watshara Shoombuatong:
Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides. J. Comput. Aided Mol. Des. 36(11): 781-796 (2022) - [j15]Teerawat Kamnardsiri, Phasit Charoenkwan, Chommaphat Malang, Ratapol Wudhikarn:
1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network Approaches. Sensors 22(22): 8788 (2022) - 2021
- [j14]Phasit Charoenkwan, Wararat Chiangjong, Chanin Nantasenamat, Md. Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong:
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides. Briefings Bioinform. 22(6) (2021) - [j13]Phasit Charoenkwan, Chanin Nantasenamat, Md. Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong:
BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides. Bioinform. 37(17): 2556-2562 (2021) - [j12]Aijaz Ahmad Malik, Warot Chotpatiwetchkul, Chuleeporn Phanus-umporn, Chanin Nantasenamat, Phasit Charoenkwan, Watshara Shoombuatong:
StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors. J. Comput. Aided Mol. Des. 35(10): 1037-1053 (2021) - 2020
- [j11]Phasit Charoenkwan, Chanin Nantasenamat, Md. Mehedi Hasan, Watshara Shoombuatong:
Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation. J. Comput. Aided Mol. Des. 34(10): 1105-1116 (2020) - [j10]Phasit Charoenkwan, Janchai Yana, Chanin Nantasenamat, Md. Mehedi Hasan, Watshara Shoombuatong:
iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides. J. Chem. Inf. Model. 60(12): 6666-6678 (2020) - [c9]Orawit Thinnukool, Phasit Charoenkwan, Pattaraporn Khuwuthyakorn, Pachara Tinamat:
Word Cloud Analysis of Customer Satisfaction in Cosmetic Products in Thailand. ICCDA 2020: 179-182
2010 – 2019
- 2018
- [j9]Papangkorn Inkeaw, Jakramate Bootkrajang, Phasit Charoenkwan, Sanparith Marukatat, Shinn-Ying Ho, Jeerayut Chaijaruwanich:
Recognition-based character segmentation for multi-level writing style. Int. J. Document Anal. Recognit. 21(1-2): 21-39 (2018) - [c8]Phasit Charoenkwan:
ThaiFBDeep: A Sentimental Analysis Using Deep Learning Combined with Bag-of-Words Features on Thai Facebook Data. IIAI-AAI 2018: 565-569 - 2017
- [j8]Papangkorn Inkeaw, Phasit Charoenkwan, Hui-Ling Huang, Sanparith Marukatat, Shinn-Ying Ho, Jeerayut Chaijaruwanich:
Recognition of handwritten Lanna Dhamma characters using a set of optimally designed moment features. Int. J. Document Anal. Recognit. 20(4): 259-274 (2017) - [c7]Vimolboon Cherapanukorn, Phasit Charoenkwan:
Word Cloud of Online Hotel Reviews in Myanmar for Customer Satisfaction Analysis. IIAI-AAI 2017: 447-452 - 2016
- [c6]Papangkorn Inkeaw, Jakramate Bootkrajang, Phasit Charoenkwan, Sanparith Marukatat, Shinn-Ying Ho, Jeerayut Chaijaruwanich:
Rule-Based Page Segmentation for Palm Leaf Manuscript on Color Image. ICADL 2016: 127-136 - 2015
- [j7]Hui-Ling Huang, Yu-Chung Wu, Li-Jen Su, Yun-Ju Huang, Phasit Charoenkwan, Wen-Liang Chen, Hua-Chin Lee, William C. Chu, Shinn-Ying Ho:
Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data. BMC Bioinform. 16: 54:1-54:14 (2015) - [j6]Tamara Vasylenko, Yi-Fan Liou, Hong-An Chen, Phasit Charoenkwan, Hui-Ling Huang, Shinn-Ying Ho:
SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method. BMC Bioinform. 16(S-1): S8 (2015) - [j5]Yerukala Sathipati Srinivasulu, Jyun-Rong Wang, Kai-Ti Hsu, Ming-Ju Tsai, Phasit Charoenkwan, Wen-Lin Huang, Hui-Ling Huang, Shinn-Ying Ho:
Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes. BMC Bioinform. 16(S18): S14 (2015) - [c5]Sujitra Arwatchananukul, Phasit Charoenkwan, Dan Xu:
POC: Paphiopedilum Orchid Classifier. ICCI*CC 2015: 206-212 - 2014
- [j4]Yi-Fan Liou, Phasit Charoenkwan, Yerukala Sathipati Srinivasulu, Tamara Vasylenko, Shih-Chung Lai, Hua-Chin Lee, Yi-Hsiung Chen, Hui-Ling Huang, Shinn-Ying Ho:
SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides. BMC Bioinform. 15(S-16): S4 (2014) - 2013
- [j3]Phasit Charoenkwan, Eric Hwang, Robert W. Cutler, Hua-Chin Lee, Li-Wei Ko, Hui-Ling Huang, Shinn-Ying Ho:
HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening. BMC Bioinform. 14(S-16): S12 (2013) - [c4]Hui-Ling Huang, Ming-Hsin Hsu, Hua-Chin Lee, Phasit Charoenkwan, Shinn-Jang Ho, Shinn-Ying Ho:
Prediction of Mouse Senescence from HE-Stain Liver Images Using an Ensemble SVM Classifier. ACIIDS (2) 2013: 325-334 - [c3]Watshara Shoombuatong, Hui-Ling Huang, Jeerayut Chaijaruwanich, Phasit Charoenkwan, Hua-Chin Lee, Shinn-Ying Ho:
Predicting protein crystallization using a simple scoring card method. CIBCB 2013: 23-30 - [c2]Hui-Ling Huang, Yerukala Sathipati Srinivasulu, Phasit Charoenkwan, Hua-Chin Lee, Shinn-Ying Ho:
Designing predictors of halophilic and non-halophilic proteins using support vector machines. CIBCB 2013: 230-237 - 2012
- [j2]Hui-Ling Huang, Phasit Charoenkwan, Te-Fen Kao, Hua-Chin Lee, Fang-Lin Chang, Wen-Lin Huang, Shinn-Jang Ho, Li-Sun Shu, Wen-Liang Chen, Shinn-Ying Ho:
Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition. BMC Bioinform. 13(S-17): S3 (2012) - 2011
- [j1]Shinn-Ying Ho, Chih-Yuan Chao, Hui-Ling Huang, Tzai-Wen Chiu, Phasit Charoenkwan, Eric Hwang:
NeurphologyJ: an automatic neuronal morphology quantification method and its application in pharmacological discovery. BMC Bioinform. 12: 230 (2011)
2000 – 2009
- 2006
- [c1]Phasit Charoenkwan, Aompilai Manorat, Jeerayut Chaijaruwanich, Sukon Prasitwattanaseree, Sakarindr Bhumiratana:
DNA Microarray Data Clustering by Hidden Markov Models and Bayesian Information Criterion. ADMA 2006: 827-834
Coauthor Index
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