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Triclustering Implementation Using Hybrid δ -Trimax Particle Swarm Optimization and Gene Ontology Analysis on Three-Dimensional Gene Expression Data

Author

Listed:
  • Titin Siswantining

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia)

  • Maria Armelia Sekar Istianingrum

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia)

  • Saskya Mary Soemartojo

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia)

  • Devvi Sarwinda

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia)

  • Noval Saputra

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia)

  • Setia Pramana

    (Politeknik Statistika STIS, Jakarta 13330, Indonesia)

  • Rully Charitas Indra Prahmana

    (Mathematics Education Department, Universitas Ahmad Dahlan, Yogyakarta 55166, Indonesia)

Abstract

Triclustering is a data mining method for grouping data based on similar characteristics. The main purpose of a triclustering analysis is to obtain an optimal tricluster, which has a minimum mean square residue (MSR) and a maximum tricluster volume. The triclustering method has been developed using many approaches, such as an optimization method. In this study, hybrid δ -Trimax particle swarm optimization was proposed for use in a triclustering analysis. In general, hybrid δ -Trimax PSO consist of two phases: initialization of the population using a node deletion algorithm in the δ -Trimax method and optimization of the tricluster using the binary PSO method. This method, when implemented on three-dimensional gene expression data, proved useful as a Motexafin gadolinium (MGd) treatment for plateau phase lung cancer cells. For its implementation, a tricluster that potentially consisted of a group of genes with high specific response to MGd was obtained. This type of tricluster can then serve as a guideline for further research related to the development of MGd drugs as anti-cancer therapy.

Suggested Citation

  • Titin Siswantining & Maria Armelia Sekar Istianingrum & Saskya Mary Soemartojo & Devvi Sarwinda & Noval Saputra & Setia Pramana & Rully Charitas Indra Prahmana, 2023. "Triclustering Implementation Using Hybrid δ -Trimax Particle Swarm Optimization and Gene Ontology Analysis on Three-Dimensional Gene Expression Data," Mathematics, MDPI, vol. 11(19), pages 1-15, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4219-:d:1256293
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