Phasit Charoenkwan

2.9k total citations
84 papers, 2.2k citations indexed

About

Phasit Charoenkwan is a scholar working on Molecular Biology, Computational Theory and Mathematics and Microbiology. According to data from OpenAlex, Phasit Charoenkwan has authored 84 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Molecular Biology, 12 papers in Computational Theory and Mathematics and 11 papers in Microbiology. Recurrent topics in Phasit Charoenkwan's work include Machine Learning in Bioinformatics (43 papers), vaccines and immunoinformatics approaches (17 papers) and RNA and protein synthesis mechanisms (16 papers). Phasit Charoenkwan is often cited by papers focused on Machine Learning in Bioinformatics (43 papers), vaccines and immunoinformatics approaches (17 papers) and RNA and protein synthesis mechanisms (16 papers). Phasit Charoenkwan collaborates with scholars based in Thailand, Taiwan and Australia. Phasit Charoenkwan's co-authors include Watshara Shoombuatong, Chanin Nantasenamat, Md Mehedi Hasan, Balachandran Manavalan, Nalini Schaduangrat, Mohammad Ali Moni, Shinn‐Ying Ho, Janchai Yana, Píetro Lió and Hui-Ling Huang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Phasit Charoenkwan

80 papers receiving 2.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Phasit Charoenkwan Thailand 27 1.6k 326 297 263 210 84 2.2k
Md Mehedi Hasan Japan 34 2.6k 1.6× 321 1.0× 258 0.9× 394 1.5× 200 1.0× 96 3.0k
Hui Ding China 51 7.5k 4.7× 47 0.1× 759 2.6× 407 1.5× 161 0.8× 135 8.2k
Jorng‐Tzong Horng Taiwan 28 1.4k 0.9× 28 0.1× 86 0.3× 194 0.7× 63 0.3× 135 2.6k
Kiyoko F. Aoki‐Kinoshita Japan 28 2.3k 1.5× 125 0.4× 73 0.2× 26 0.1× 62 0.3× 112 2.7k
Junfeng Xia China 30 2.2k 1.4× 19 0.1× 418 1.4× 143 0.5× 51 0.2× 139 3.2k
Yongchun Zuo China 31 2.1k 1.4× 24 0.1× 181 0.6× 78 0.3× 40 0.2× 127 2.8k
Pengmian Feng China 32 4.8k 3.0× 18 0.1× 485 1.6× 264 1.0× 52 0.2× 60 4.9k
Farman Ali Pakistan 30 1.7k 1.1× 12 0.0× 441 1.5× 252 1.0× 110 0.5× 90 3.0k
Guohua Wang China 31 3.1k 1.9× 27 0.1× 341 1.1× 39 0.1× 48 0.2× 184 3.9k

Countries citing papers authored by Phasit Charoenkwan

Since Specialization
Citations

This map shows the geographic impact of Phasit Charoenkwan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Phasit Charoenkwan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Phasit Charoenkwan more than expected).

Fields of papers citing papers by Phasit Charoenkwan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Phasit Charoenkwan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Phasit Charoenkwan. The network helps show where Phasit Charoenkwan may publish in the future.

Co-authorship network of co-authors of Phasit Charoenkwan

This figure shows the co-authorship network connecting the top 25 collaborators of Phasit Charoenkwan. A scholar is included among the top collaborators of Phasit Charoenkwan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Phasit Charoenkwan. Phasit Charoenkwan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Charoenkwan, Phasit, Nalini Schaduangrat, Pramote Chumnanpuen, & Watshara Shoombuatong. (2025). PSR‐MAPMS: A new approach for the interpretable prediction of myelin autoantigenic peptides in multiple sclerosis using multi‐source propensity scores. Protein Science. 34(8). e70010–e70010. 1 indexed citations
2.
Charoenkwan, Phasit, et al.. (2024). M3S-ALG: Improved and robust prediction of allergenicity of chemical compounds by using a novel multi-step stacking strategy. Future Generation Computer Systems. 162. 107455–107455. 11 indexed citations
3.
Xu, Dan, et al.. (2024). Implementing a deep learning model for defect classification in Thai Arabica green coffee beans. SHILAP Revista de lepidopterología. 9. 100680–100680. 4 indexed citations
4.
Charoenkwan, Phasit, Nalini Schaduangrat, & Watshara Shoombuatong. (2023). StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens. BMC Bioinformatics. 24(1). 301–301. 5 indexed citations
5.
6.
Charoenkwan, Phasit, et al.. (2023). TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides. BMC Bioinformatics. 24(1). 356–356. 10 indexed citations
7.
Charoenkwan, Phasit, et al.. (2023). PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning. Computers in Biology and Medicine. 158. 106784–106784. 14 indexed citations
8.
Charoenkwan, Phasit, et al.. (2022). 1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network Approaches. Sensors. 22(22). 8788–8788. 4 indexed citations
9.
Charoenkwan, Phasit, Wararat Chiangjong, Chanin Nantasenamat, et al.. (2022). SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids. Pharmaceutics. 14(1). 122–122. 19 indexed citations
10.
Charoenkwan, Phasit, Nalini Schaduangrat, Mohammad Ali Moni, et al.. (2022). SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins. Computers in Biology and Medicine. 146. 105704–105704. 44 indexed citations
11.
Charoenkwan, Phasit, Chanin Nantasenamat, Md Mehedi Hasan, et al.. (2022). PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning. Computers in Biology and Medicine. 152. 106368–106368. 17 indexed citations
12.
Charoenkwan, Phasit, Pramote Chumnanpuen, Nalini Schaduangrat, et al.. (2022). Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides. Journal of Computer-Aided Molecular Design. 36(11). 781–796. 16 indexed citations
13.
Charoenkwan, Phasit, Chanin Nantasenamat, Md Mehedi Hasan, et al.. (2021). iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features. International Journal of Molecular Sciences. 22(16). 8958–8958. 42 indexed citations
14.
Charoenkwan, Phasit, et al.. (2021). A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides. Scientific Reports. 11(1). 23782–23782. 38 indexed citations
15.
Charoenkwan, Phasit, Janchai Yana, Chanin Nantasenamat, Md Mehedi Hasan, & Watshara Shoombuatong. (2020). iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides. Journal of Chemical Information and Modeling. 60(12). 6666–6678. 139 indexed citations
16.
Charoenkwan, Phasit, Nuttapat Anuwongcharoen, Chanin Nantasenamat, Md Mehedi Hasan, & Watshara Shoombuatong. (2020). In Silico Approaches for the Prediction and Analysis of Antiviral Peptides: A Review. Current Pharmaceutical Design. 27(18). 2180–2188. 28 indexed citations
17.
Charoenkwan, Phasit, Sakawrat Kanthawong, Chanin Nantasenamat, Md Mehedi Hasan, & Watshara Shoombuatong. (2020). iDPPIV-SCM: A Sequence-Based Predictor for Identifying and Analyzing Dipeptidyl Peptidase IV (DPP-IV) Inhibitory Peptides Using a Scoring Card Method. Journal of Proteome Research. 19(10). 4125–4136. 81 indexed citations
18.
Charoenkwan, Phasit, Janchai Yana, Nalini Schaduangrat, et al.. (2020). iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides. Genomics. 112(4). 2813–2822. 107 indexed citations
19.
Charoenkwan, Phasit, Nalini Schaduangrat, Chanin Nantasenamat, Theeraphon Piacham, & Watshara Shoombuatong. (2019). iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides Using Informative Physicochemical Properties. International Journal of Molecular Sciences. 21(1). 75–75. 70 indexed citations
20.
Charoenkwan, Phasit, et al.. (2012). Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition. BMC Bioinformatics. 13(S17). S3–S3. 77 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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