Biclustering methods for one-mode asymmetric matrices.

Biclustering Methods for Re-ordering Data Matrices in Systems Biology, Drug Discovery and Toxicology

An Approximation Ratio for Biclustering

The problem of biclustering consists of the simultaneous clustering of rows and columns of a matrix such that each of the submatrices induced by a pair of row and column clusters is as uniform as possible. In this paper we approximate the optimal biclustering by applying one-way clustering algorithms independently on the rows and on the columns of the input matrix. We show that such a solution ...

Gene co-expression networks via biclustering Differential gene co-expression networks via Bayesian biclustering models

Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-re...

Asymmetric biclustering with constrained von Mises-Fisher models

As a probability distribution on the high-dimensional sphere, the von Mises-Fisher (vMF) distribution is widely used for directional statistics and data analysis methods based on correlation. We consider a constrained vMF distribution for block modeling, which provides a probabilistic model of an asymmetric biclustering method that uses correlation as the similarity measure of data features. We...

Optimal Estimation and Completion of Matrices with Biclustering Structures

Biclustering structures in data matrices were first formalized in a seminal paper by John Hartigan [15] where one seeks to cluster cases and variables simultaneously. Such structures are also prevalent in block modeling of networks. In this paper, we develop a theory for the estimation and completion of matrices with biclustering structures, where the data is a partially observed and noise cont...