The goal of biclustering in a gene expression data matrix is to find a submatrix such that the genes in the submatrix show highly correlated activities across all conditions in the submatrix. A measure called Mean Squared Residue (MSR) is used to simultaneously evaluate the coherence of rows and columns within a submatrix. In this paper a new method for biclustering gene expression data is deve...

Biclustering aims to mine a number of co-expressed genes under a set of experimental conditions in gene expression dataset. Recently, differential co-expression biclustering approach has been used to identify class-specific biclusters between two gene expression datasets. However, it cannot handle differential co-expression constant row biclusters efficiently in real-valued datasets. In this pa...

Cheng and Church algorithm is an important approach in biclustering algorithms. In this paper, the process of the extended space in the second stage of Cheng and Church algorithm is improved and the selections of two important parameters are discussed. The results of the improved algorithm used in the gene expression spectrum analysis show that, compared with Cheng and Church algorithm, the qua...

There are lots of validation indexes and techniques to study clustering results. Biclustering algorithms have been applied in Systems Biology, principally in DNA Microarray analysis, for the last years, with great success. Nowadays, there is a big set of biclustering algorithms each one based in different concepts, but there are few intercomparisons that measure their performance. We review and...

Motivation Biclustering has become a major tool for analyzing large datasets given as matrix of samples times features and has been successfully applied in life sciences and e-commerce for drug design and recommender systems, respectively. actor nalysis for cluster cquisition (FABIA), one of the most successful biclustering methods, is a generative model that represents each bicluster by two sp...

In the last few years the gene expression microarray technology has become a central tool in the field of functional genomics in which the expression levels of thousands of genes in a biological sample are determined in a single experiment. Several clustering and biclustering methods have been introduced to analyze the gene expression data by identifying the similar patterns and grouping genes ...

Biclustering is a vital data mining tool which is commonly employed on microarray data sets for analysis task in bioinformat ics research and medical applications. There has been extensive research on biclustering of gene expression data arising from microarray experiment. This technique is an important analysis tool in gene expression measurement, when some genes have multip le functions and e...

Query driven Biclustering Model refers to the problem of extracting biclusters based on a query gene or query condition. The extracted biclusters consist of a set of genes and a subset of conditions that are similar to the query gene or query condition and it includes the query input also. Two approaches applied for biclustering problems are topdown and bottom-up, based on how they tackle the p...