Triclustering Implementation Using Hybrid δ-Trimax Particle Swarm Optimization and Gene Ontology Analysis on Three-Dimensional Gene Expression Data
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Published:2023-10-09
Issue:19
Volume:11
Page:4219
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ISSN:2227-7390
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Container-title:Mathematics
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language:en
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Short-container-title:Mathematics
Author:
Siswantining Titin1ORCID, Istianingrum Maria Armelia Sekar1, Soemartojo Saskya Mary1, Sarwinda Devvi1, Saputra Noval1, Pramana Setia2, Prahmana Rully Charitas Indra3ORCID
Affiliation:
1. Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia 2. Politeknik Statistika STIS, Jakarta 13330, Indonesia 3. 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.
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
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