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Portrait of Kitiporn Plaimas
Faculty

Kitiporn Plaimas

Associate Professor Ph.D.
Personal site pioneer.chula.ac.th S Scopus® Email
Office
MATH 1108/4
Phone
02-218-7114
Email
kitiporn.p@chula.ac.th
Fax
02-255-2287

Research Areas

Algorithms, Optimization, Parallel Computing and Theoretical Computer Science Artificial Intelligence and Data Science Bioinformatics and Computational Biology Graph and Network Analysis Mathematical Modeling
  • Bioinformatics and Computational Biology
  • Systems Biology
  • Network Modelling
  • Mathematical Modelling
  • Machine Learning
  • Data Mining
  • Artificail Intelligent
  • Graph and Network Analysus

Education

  1. 2012
    Ph.D.
    Applied Mathematics and Computer Science
    Heidelberg University, Germany
  2. 2005
    M.Sc.
    Computational Science
    Chulalongkorn University, Thailand
  3. 2002
    B.Sc. (Hons)
    Mathematics
    Mahidol University, Thailand

Publications

  • 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.
   
View on Scopus® Semantic Scholar Orcid

Books 

Book chapters
Plaimas, K. and König, R. (2017)  Identifying Antimalarial Drug Targets by Cellular Network Analysis, in Current Topics in Malaria (Edited by Alfonso J. Rodriguez-Morales), InTechOpen, pages 267-283.
Plaimas, K. and König, R. (2011) Machine Learning Methods for Identifying Essential Genes and Proteins in Networks, in Applied Statistics for Network Biology: Methods in Systems Biology (eds M. Dehmer, F. Emmert-Streib, A. Graber and A. Salvador), Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany. doi: 10.1002/9783527638079.ch10, pages 201-214