Motivation:During the last years, the discovering of biclusters in data is becoming more and more popular. Biclustering aims at extracting a set of clusters, each of which might use a different subset of attributes. Therefore, it is clear that the usefulness of biclustering techniques is beyond the traditional clustering techniques, especially when datasets present high or very high dimensionality. Also, biclustering considers overlapping, which is an interesting aspect, algorithmically and from the point of view of the result interpretation. Since the Cheng and Church's works, the mean squared residue has turned into one of the most popular measures to search for biclusters, which ideally should discover shifting and scaling patterns
Motivation: Biclustering has been emerged as a powerful tool for identification of a group of co-exp...
Computational Biology is the research are that contributes to the analysis of biological data throug...
In this paper, we propose a new model for coherent clustering of gene expression data called reg-clu...
In DNA microarray experiments, discovering groups of genes that share similar transcriptional charac...
Aim of clustering of data is to analyze gene expression data. Recently, biclustering or simultaneous...
Abstract- Microarray technology is a powerful method for monitoring the expression level of thousand...
Analysis of large scale geonomics data, notably gene expression, has initially focused on clustering...
Biclustering or simultaneous clustering of both genes and conditions has generated considerable inte...
Abstract Background Biclusteri...
Biclustering or simultaneous clustering of both genes and conditions have generated considerable int...
The need to analyze high-dimension biological data is driv-ing the development of new data mining me...
Biclustering is a powerful data mining technique that allows clustering of rows and columns, simulta...
<div><p>Biclustering is the simultaneous clustering of two related dimensions, for example, of indiv...
A good number of biclustering algorithms have been proposed for grouping gene expression data. Many ...
DNA microarray technologies are used extensively to profile the expression levels of thousands of ge...
Motivation: Biclustering has been emerged as a powerful tool for identification of a group of co-exp...
Computational Biology is the research are that contributes to the analysis of biological data throug...
In this paper, we propose a new model for coherent clustering of gene expression data called reg-clu...
In DNA microarray experiments, discovering groups of genes that share similar transcriptional charac...
Aim of clustering of data is to analyze gene expression data. Recently, biclustering or simultaneous...
Abstract- Microarray technology is a powerful method for monitoring the expression level of thousand...
Analysis of large scale geonomics data, notably gene expression, has initially focused on clustering...
Biclustering or simultaneous clustering of both genes and conditions has generated considerable inte...
Abstract Background Biclusteri...
Biclustering or simultaneous clustering of both genes and conditions have generated considerable int...
The need to analyze high-dimension biological data is driv-ing the development of new data mining me...
Biclustering is a powerful data mining technique that allows clustering of rows and columns, simulta...
<div><p>Biclustering is the simultaneous clustering of two related dimensions, for example, of indiv...
A good number of biclustering algorithms have been proposed for grouping gene expression data. Many ...
DNA microarray technologies are used extensively to profile the expression levels of thousands of ge...
Motivation: Biclustering has been emerged as a powerful tool for identification of a group of co-exp...
Computational Biology is the research are that contributes to the analysis of biological data throug...
In this paper, we propose a new model for coherent clustering of gene expression data called reg-clu...