Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Information-theoretic co-clustering
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)
Neuroinformatics for Genome-Wide 3-D Gene Expression Mapping in the Mouse Brain
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
The Journal of Machine Learning Research
Computers and Operations Research
Introduction to Information Retrieval
Introduction to Information Retrieval
Coclustering of Human Cancer Microarrays Using Minimum Sum-Squared Residue Coclustering
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A sparsity-inducing formulation for evolutionary co-clustering
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A Bayesian factorised covariance model for image analysis
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The mammalian brain contains cells of a large variety of types. The phenotypic properties of cells of different types are largely the results of distinct gene expression patterns. Therefore, it is of critical importance to characterize the gene expression patterns in the mammalian brain. The Allen Developing Mouse Brain Atlas provides spatiotemporal in situ hybridization gene expression data across multiple stages of mouse brain development. It provides a framework to explore spatiotemporal regulation of gene expression during development. We employ a graph approximation formulation to cocluster the genes and the brain voxels simultaneously for each time point. We show that this formulation can be expressed as a probabilistic latent semantic analysis (PLSA) model, thereby allowing us to use the expectation-maximization algorithm for PLSA to estimate the coclustering parameters. To provide a quantitative comparison with prior methods, we evaluate the coclustering method on a set of standard synthetic data sets. Results indicate that our method consistently outperforms prior methods. We apply our method to cocluster the Allen Developing Mouse Brain Atlas data. Results indicate that our clustering of voxels is more consistent with classical neuroanatomy than those of prior methods. Our analysis also yields sets of genes that are co-expressed in a subset of the brain voxels.