Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Pattern classification in DNA microarray data of multiple tumor types
Pattern Recognition
Extracting gene regulation information for cancer classification
Pattern Recognition
Bioinformatics
Impact of imputation of missing values on classification error for discrete data
Pattern Recognition
Ensemble gene selection for cancer classification
Pattern Recognition
EURASIP Journal on Bioinformatics and Systems Biology
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DNA microarray experiment inevitably generates gene expression data with missing values. An important and necessary pre-processing step is thus to impute these missing values. Existing imputation methods exploit gene correlation among all experimental conditions for estimating the missing values. However, related genes coexpress in subsets of experimental conditions only. In this paper, we propose to use biclusters, which contain similar genes under subset of conditions for characterizing the gene similarity and then estimating the missing values. To further improve the accuracy in missing value estimation, an iterative framework is developed with a stopping criterion on minimizing uncertainty. Extensive experiments have been conducted on artificial datasets, real microarray datasets as well as one non-microarray dataset. Our proposed biclusters-based approach is able to reduce errors in missing value estimation.