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

Titin Siswantining (), Maria Armelia Sekar Istianingrum, Saskya Mary Soemartojo, Devvi Sarwinda, Noval Saputra, Setia Pramana and Rully Charitas Indra Prahmana
Additional contact information
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

Mathematics, 2023, vol. 11, issue 19, 1-15

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.

Keywords: mean square residue; optimization; triclustering quality index; microarray (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/19/4219/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/19/4219/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:19:p:4219-:d:1256293

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().