TY - JOUR
T1 - Triclustering method for finding biomarkers in human immunodeficiency virus-1 gene expression data
AU - Siswantining, Titin
AU - Bustamam, Alhadi
AU - Sarwinda, Devvi
AU - Soemartojo, Saskya Mary
AU - Abdul Latief, Moh
AU - Octaria, Elke Annisa
AU - Siregar, Anggrainy Togi Marito
AU - Septa, Oon
AU - Al-Ash, Herley Shaori
AU - Saputra, Noval
N1 - Funding Information:
NKB-035/UN2.F3/HKP.05.00/2021 is the number of the research grant provided by FMIPA Universitas Indonesia.
Publisher Copyright:
© 2022 American Institute of Mathematical Sciences. All rights reserved.
PY - 2022
Y1 - 2022
N2 - HIV-1 is a virus that destroys CD4 + cells in the body's immune system, causing a drastic decline in immune system performance. Analysis of HIV-1 gene expression data is urgently needed. Microarray technology is used to analyze gene expression data by measuring the expression of thousands of genes in various conditions. The gene expression series data, which are formed in three dimensions, are analyzed using triclustering. Triclustering is an analysis technique for 3D data that aims to group data simultaneously into rows and columns across different times/conditions. The result of this technique is called a tricluster. A tricluster is a subspace in the form of a subset of rows, columns, and time/conditions. In this study, we used the δ-Trimax, THD Tricluster, and MOEA methods by applying different measures, namely, transposed virtual error, the New Residue Score, and the Multi Slope Measure. The gene expression data consisted of 22,283 probe gene IDs, 40 observations, and four conditions: normal, acute, chronic, and non-progressor. Tricluster evaluation was carried out based on intertemporal homogeneity. An analysis of the probe ID gene that affects AIDS was carried out through this triclustering process. Based on this analysis, a gene symbol which is biomarkers associated with AIDS due to HIV-1, HLA-C, was found in every condition for normal, acute, chronic, and non-progressive HIV-1 patients.
AB - HIV-1 is a virus that destroys CD4 + cells in the body's immune system, causing a drastic decline in immune system performance. Analysis of HIV-1 gene expression data is urgently needed. Microarray technology is used to analyze gene expression data by measuring the expression of thousands of genes in various conditions. The gene expression series data, which are formed in three dimensions, are analyzed using triclustering. Triclustering is an analysis technique for 3D data that aims to group data simultaneously into rows and columns across different times/conditions. The result of this technique is called a tricluster. A tricluster is a subspace in the form of a subset of rows, columns, and time/conditions. In this study, we used the δ-Trimax, THD Tricluster, and MOEA methods by applying different measures, namely, transposed virtual error, the New Residue Score, and the Multi Slope Measure. The gene expression data consisted of 22,283 probe gene IDs, 40 observations, and four conditions: normal, acute, chronic, and non-progressor. Tricluster evaluation was carried out based on intertemporal homogeneity. An analysis of the probe ID gene that affects AIDS was carried out through this triclustering process. Based on this analysis, a gene symbol which is biomarkers associated with AIDS due to HIV-1, HLA-C, was found in every condition for normal, acute, chronic, and non-progressive HIV-1 patients.
KW - biclustering
KW - genetic based
KW - multi slope measure
KW - thd-tricluster
KW - transposed virtual error
UR - http://www.scopus.com/inward/record.url?scp=85130749912&partnerID=8YFLogxK
U2 - 10.3934/mbe.2022318
DO - 10.3934/mbe.2022318
M3 - Article
AN - SCOPUS:85130749912
SN - 1547-1063
VL - 19
SP - 6743
EP - 6763
JO - Mathematical Biosciences and Engineering
JF - Mathematical Biosciences and Engineering
IS - 7
ER -
