Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Discovering local structure in gene expression data: the order-preserving submatrix problem
Proceedings of the sixth annual international conference on Computational biology
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering of Expression Data Using Simulated Annealing
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
An Efficient Constraint-Based Closed Set Mining Algorithm
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
An effective algorithm for mining 3-clusters in vertically partitioned data
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Co-clustering: A Versatile Tool for Data Analysis in Biomedical Informatics
IEEE Transactions on Information Technology in Biomedicine
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High-throughput sequencing (CHIP-Seq) data exhibit binding events with possible binding locations and their strengths, followed by interpretations of the locations of peaks. Recent methods tend to summarize all CHIP-Seq peaks detected within a limited up and down region of each gene into one real-valued score in order to quantify the probability of regulation in a region. Applying subspace clustering (or biclustering) techniques on these scores would discover important knowledge such as the potential co-regulation or co-factors mechanisms. The ideal biclusters generated should contain subsets of genes, and transcription factors (TF) such that the cell-values in biclusters are distributed around a mean value with low variance. Such biclusters would indicate TF sets regulating gene sets with the same probability values. However, most existing biclustering algorithms are neither able to enforce variance as a strict limitation on the values contained in a bicluster, nor use variance as the guiding metric while searching for the desirable biclusters. An algorithm that uses search spaces defined by lattices containing all overlapping biclusters and a bound on variance values as the guiding metric is presented in this paper. The algorithm is shown to be an efficient and effective method for discovering the possibly overlapping biclusters under pre-defined variance bounds. We present in this paper our algorithm, its results with synthetic and CHIP-Seq and motif datasets, and compare them with the results obtained by other algorithms to demonstrate the power and effectiveness of our algorithm.