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Submitted
February 14, 2025
Accepted
May 8, 2025
Published
April 30, 2025
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Abstract

Type 2 diabetes mellitus (T2DM) is a metabolic disorder primarily driven by insulin resistance, involving complex genetic regulation. Understanding the molecular mechanisms underlying insulin resistance is crucial for identifying therapeutic targets. This study compared the performance of two biclustering algorithms, factor analysis for bicluster acquisition (FABIA) and the Cheng and Church algorithm (CCA), in analyzing gene expression data associated with insulin resistance. Using the GSE19420 dataset, simulated missing values were introduced to evaluate the robustness of both methods. Results showed that CCA consistently achieved lower mean squared error (MSE) in reconstructing gene expression patterns, suggesting higher accuracy in capturing co-expression structures. Nevertheless, FABIA effectively detected sparse, biologically relevant clusters. Notably, key genes such as MYO5B, DLG2, AXIN2, and PTK7 were identified within the biclusters, supporting their involvement in insulin signaling and metabolic regulation. These findings underscore the need to select biclustering methods that align with specific analytical goals and offer insights into gene networks involved in insulin resistance.

Keywords

Biclustering Gene Expression Mean Squared Error Insulin Resistance Missing Value Imputation

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How to Cite
Soemarso, D. R., Siswantining, T., & Pramana, S. (2025). Genetic Cluster Analysis of Insulin Resistance Using KNN Imputation and FABIA-CCA Biclustering . Enthusiastic : International Journal of Applied Statistics and Data Science, 5(1), 101–106. https://doi.org/10.20885/enthusiastic.vol5.iss1.art10
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