TY - JOUR
T1 - Triclustering Implementation Using Hybrid δ-Trimax Particle Swarm Optimization and Gene Ontology Analysis on Three-Dimensional Gene Expression Data
AU - Siswantining, Titin
AU - Istianingrum, Maria Armelia Sekar
AU - Soemartojo, Saskya Mary
AU - Sarwinda, Devvi
AU - Saputra, Noval
AU - Pramana, Setia
AU - Prahmana, Rully Charitas Indra
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/10
Y1 - 2023/10
N2 - 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 (Formula presented.) -Trimax particle swarm optimization was proposed for use in a triclustering analysis. In general, hybrid (Formula presented.) -Trimax PSO consist of two phases: initialization of the population using a node deletion algorithm in the (Formula presented.) -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.
AB - 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 (Formula presented.) -Trimax particle swarm optimization was proposed for use in a triclustering analysis. In general, hybrid (Formula presented.) -Trimax PSO consist of two phases: initialization of the population using a node deletion algorithm in the (Formula presented.) -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.
KW - mean square residue
KW - microarray
KW - optimization
KW - triclustering quality index
UR - http://www.scopus.com/inward/record.url?scp=85176419592&partnerID=8YFLogxK
U2 - 10.3390/math11194219
DO - 10.3390/math11194219
M3 - Article
AN - SCOPUS:85176419592
SN - 2227-7390
VL - 11
JO - Mathematics
JF - Mathematics
IS - 19
M1 - 4219
ER -