List of Selected PUBLICATIONS (Last Three Years)

Full Articles (International Journals)

1. Abdullahi MS, Suratanee A, Piro RM, and Plaimas K., (2024), Persistent Homology Identifies Pathways Associated with Hepatocellular Carcinoma from Peripheral Blood Samples, Mathematics 2024, 12(5), 725.
2. Sonsungsan P, Suratanee A, Buaboocha T, Chadchawan S, Plaimas K. (2024), Identification of Salt-Sensitive and Salt-Tolerant Genes through Weighted Gene Co-Expression Networks across Multiple Datasets: A Centralization and Differential Correlation Analysis. Genes (Basel). 2024;15(3):316.
3. Paklao, T., Suratanee, A., Plaimas, K. (2023), ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology, BMC bioinformatics 24 (1), 492
4. Janyasupab, P., Suratanee, A., Plaimas, K. (2023), GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data, November 2023, PeerJ Computer Science 9(1):e1686, DOI: 10.7717/peerj-cs.1686
5. Suratanee, A. and Plaimas, K. (2023), Gene Association Classification for Autism Spectrum Disorder: Leveraging Gene Embedding and Differential Gene Expression Profiles to Identify Disease-Related Genes, Applied Sciences (Switzerland), 2023, 13(15), 8980
6. Intarapanya, T., Suratanee, A., Pattaradilokrat, S., Plaimas, K. (2023), Modeling the Spread of COVID-19 with the Control of Mixed Vaccine Types during the Pandemic in Thailand, Trop. Med. Infect. Dis. 2023, 8(3), 175, https://doi.org/10.3390/tropicalmed8030175.
7. Tangmanussukum, P., Kawichai, T., Suratanee, A., Plaimas, K. (2022), Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction, October 2022, PeerJ Computer Science 8(5):e1124, DOI:10.7717/peerj-cs.1124
8. Intarapanya, T., Suratanee, A., Pattaradilokrat, S., Plaimas, K. (2022), Modeling the spread of COVID-19 as a consequence of undocumented immigration toward the reduction of daily hospitalization: Case reports from Thailand, August 2022, PLoS ONE, 17(8):e0273558, DOI: 10.1371/journal.pone.0273558
9. Sagulkoo, P., Chuntakaruk, H., Rungrotmongkol, T., Suratanee, A., and Plaimas, K. (2022), Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine, J. Pers. Med. 2022, 12(7), 1030; https://doi.org/10.3390/jpm12071030.
10. Sonsungsan P., Chantanakool P., Suratanee A., Buaboocha T., Comai L., Chadchawan S. and Plaimas K. (2021), Identification of Key Genes in ‘Luang Pratahn’, Thai Salt-Tolerant Rice, Based on Time-Course Data and Weighted Co-expression Networks. Front. Plant Sci., (December 2021): doi.org/10.3389/fpls.2021.744654. Web of Science (ISI), SCOPUS
11. Suratanee A. and Plaimas K. (2021), Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of Plasmodium falciparum Genes. Int. J. Mol. Sci. 22(18) (September 2021): 10019. Web of Science (ISI), SCOPUS
12. Suratanee, A., Buaboocha, T., Plaimas, K. (2021), Prediction of Human–Plasmodium vivax Protein Associations from Heterogeneous Network Structures based on Machine Learning Approach, Bioinformatics and Biology Insights, 2021.
13. Janyasupab, P., Suratanee, A., Plaimas, K. (2021), Network diffusion with centrality measures to identify disease-related genes, Mathematical Biosciences and Engineering, 2021, 18(3), pp. 2909–2929.
14. Kawichai, T., Suratanee, A., Plaimas, K. (2021), Meta-Path Based Gene Ontology Profiles for Predicting Drug-Disease Associations, IEEE Access, 2021, 9, pp. 41809–41820, 9374464.