DNA microarray data always contains missing values. As subsequent analysis such as biclustering can only be applied on complete data, these missing values have to be imputed before any biclusters can be detected. Existing imputation methods exploit coherence among expression values in the microarray data. In view that biclustering attempts to find correlated expression values within the data, w...

Biclustering, which can be defined as the simultaneous clustering of rows and columns in a data matrix, has received increasing attention in recent years, particularly in the field of Bioinformatics (e.g. for the analysis of microarray data). This paper proposes a novel biclustering approach, which extends the Affinity Propagation [1] clustering algorithm to the biclustering case. In particular...

A new algorithm is presented for 4tting the plaid model, a biclustering method developed for clustering gene expression data. The approach is based on speedy individual di6erences clustering and uses binary least squares to update the cluster membership parameters, making use of the binary constraints on these parameters and simplifying the other parameter updates. The performance of both algor...

To understand complex biological systems, the research community has produced huge corpus of gene expression data. A large number of clustering approaches have been proposed for the analysis of gene expression data. However, extracting important biological knowledge is still harder. To address this task, clustering techniques are used. In this paper, hybrid Hierarchical k-Means algorithm is use...

In the biclustering problem, we seek to simultaneously group observations and features. While biclustering has applications in a wide array of domains, ranging from text mining to collaborative filtering, the problem of identifying structure in high-dimensional genomic data motivates this work. In this context, biclustering enables us to identify subsets of genes that are co-expressed only with...

As we have seen in previous lectures, the technology of DNA chips allows the measurement of mRNA levels simultaneously for thousands of genes. The abundance of readily available data allows us the option to try to extract information, not easily seen while analyzing a single test, from the results of a multitude of tests. The results of DNA chip experiments are usually organized together in a g...

Biclustering is a very useful data mining technique which identifies coherent patterns from microarray gene expression data. A bicluster of a gene expression dataset is a subset of genes which exhibit similar expression patterns along a subset of conditions. Biclustering is a powerful analytical tool for the biologist and has generated considerable interest over the past few decades. Many biclu...

Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse...

As we have seen in previous lectures, the technology of DNA chips allows the measurement of mRNA levels simultaneously for thousands of genes. The results of DNA chip experiments are usually organized together in a gene expression matrix, with rows corresponding to genes and columns corresponding to conditions. A bicluster can be defined as a submatrix spanned by a subset of genes and a subset ...

As we have seen in previous lectures, the technology of DNA chips allows the measurement of mRNA levels simultaneously for thousands of genes. The results of DNA chip experiments are usually organized together in a gene expression matrix, with rows corresponding to genes and columns corresponding to conditions. A bicluster can be defined as a submatrix spanned by a subset of genes and a subset ...