Implementation of The Sequential Biclustering Method and Centroid-Based Imputation (Fuzzy C-Means) for Missing Values Imputation
Keywords:
Cheng and Church, Mean Squared Residue, Missing Completely At Random, Missing Rate, Root Mean Squared ErrorAbstract
Missing values are a common issue in gene expression data and may significantly affect the accuracy of subsequent analyses if not properly handled. This study aims to implement and evaluate a missing value imputation method based on Sequential Biclustering and Centroid-Based Imputation with Fuzzy C-Means (FCM) on gene expression data of Type 2 Diabetes Mellitus patients. Missing values were generated under the Missing Completely At Random (MCAR) mechanism with missing rate ranging from 5% to 55%, and five replications were conducted for each missing rate. Biclustering was performed using the Cheng and Church algorithm to identify biclusters based on the Mean Squared Residue (MSR), followed by missing value estimation using FCM with 2 to 5 biclusters. The performance of the imputation method was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) by comparing the imputed values with the original data. The results indicate that the configuration with 2 biclusters consistently produces the lowest and most stable MSE, RMSE, and MAE across all missing rates.
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