Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Introduction to Linear Optimization
Introduction to Linear Optimization
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Time-Series Biclustering Algorithm for Revealing Co-Regulated Genes
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Mean squared residue based biclustering algorithms
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
Gene expression biclustering using random walk strategies
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Finding k-biclusters from gene expression data
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
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Cheng and Church proposed a greedy deletion-addition algorithm to find a given number of k biclusters, whose mean squared residues (MSRs) are below certain thresholds and the missing values in the matrix are replaced with random numbers. In our previous paper we introduced the dual biclustering method with quadratic optimization to missing data and row inversions. In this paper, we modified the dual biclustering method with quadratic optimization and added three new features. First, we introduce "row status" for each row in a bicluster where we add and also delete rows from biclusters based on their status in order to find min MSR. We compare our results with Cheng and Church's approach where they inverse rows while adding them to the biclusters. We select the row or the negated row not only at addition, but also at deletion and show improvement. Second, we give a prove for the theorem introduced by Cheng and Church in [4]. Since, missing data often occur in the given data matrices for biclustering, usually, missing data are filled by random numbers. However, we show that ignoring the missing data is a better approach and avoids additional noise caused by randomness. Since, an ideal bicluster is a bicluster with an H value of zero, our results show a significant decrease of H value of the biclusters with lesser noise compared to original dual biclustering and Cheng and Church method.