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
Quadratic programming relaxations for metric labeling and Markov random field MAP estimation
ICML '06 Proceedings of the 23rd international conference on Machine learning
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Gene expression biclustering using random walk strategies
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Mean Square Residue Biclustering with Missing Data and Row Inversions
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
An effective measure for assessing the quality of biclusters
Computers in Biology and Medicine
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The availability of large microarray data has brought along many challengesfor biological data mining. Following Cheng and Church [4], many differentbiclustering methods have been widely used to find appropriate subsets ofexperimental conditions. Still no paper directly optimizes or bounds the MeanSquared Residue (MSR) originally suggested by Cheng and Church. Their algorithm,for a given expression matrix A and an upper bound on MSR, finds kalmost non overlapping biclusters whose sizes are not predefined thus making itdifficult to compare with other methods. In this paper, we propose two new Mean Squared Residue (MSR) based biclusteringmethods. The first method is a dual biclustering algorithm which finds(k × l)-bicluster with MSR using a greedy approach. The second method combinesdual biclustering algorithm with quadratic programming. The dual biclusteringalgorithm reduces the size of the matrix, so that the quadratic programcan find an optimal bicluster reasonably fast. We control bicluster overlappingby changing the penalty for reusing cells in biclusters. The average MSR in [4]biclusterings for yeast is almost the same as for the proposed dual biclusteringwhile the median MSR is 1.5 times larger thus implying that the quadratic programfinds much better smaller biclusters.