Biclustering aims at finding subgroups of genes that show highly correlated behaviors across a subgroup of conditions. Biclustering is a very useful tool for mining microarray data and has various practical applications. From a computational point of view, biclustering is a highly combinatorial search problem and can be solved with optimization methods
In providing simultaneous information on expression profiles for thousands of genes, microarray tech...
Microarrays are one of the latest breakthroughs in experimental molecular biology, which provide a p...
Sawannee Sutheeworapong,1,2 Motonori Ota,4 Hiroyuki Ohta,1 Kengo Kinoshita2,31Department of Biologic...
There are subsets of genes that have similar behavior under subsets of conditions, so we say that th...
Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneou...
In the context of microarray data analysis, biclustering allows the simultaneous identification of a...
In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows...
In DNA microarray experiments, discovering groups of genes that share similar transcriptional charac...
Biclustering is a very useful tool for analyzing microarray data. It aims to identify maximal groups...
There are subsets of genes that have similar behavior under subsets of conditions, so we say that th...
Abstract- Microarray technology is a powerful method for monitoring the expression level of thousand...
Biclustering, which is simultaneous clustering of columns and rows in data matrix, became an issue w...
Biclustering aims at identifying several biclusters that reveal potential local patterns from a micr...
In this chapter, different methods and applications of biclustering algorithms to DNA microarray dat...
The analysis of gene expression data obtained from microarray experiments is important for discoveri...
In providing simultaneous information on expression profiles for thousands of genes, microarray tech...
Microarrays are one of the latest breakthroughs in experimental molecular biology, which provide a p...
Sawannee Sutheeworapong,1,2 Motonori Ota,4 Hiroyuki Ohta,1 Kengo Kinoshita2,31Department of Biologic...
There are subsets of genes that have similar behavior under subsets of conditions, so we say that th...
Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneou...
In the context of microarray data analysis, biclustering allows the simultaneous identification of a...
In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows...
In DNA microarray experiments, discovering groups of genes that share similar transcriptional charac...
Biclustering is a very useful tool for analyzing microarray data. It aims to identify maximal groups...
There are subsets of genes that have similar behavior under subsets of conditions, so we say that th...
Abstract- Microarray technology is a powerful method for monitoring the expression level of thousand...
Biclustering, which is simultaneous clustering of columns and rows in data matrix, became an issue w...
Biclustering aims at identifying several biclusters that reveal potential local patterns from a micr...
In this chapter, different methods and applications of biclustering algorithms to DNA microarray dat...
The analysis of gene expression data obtained from microarray experiments is important for discoveri...
In providing simultaneous information on expression profiles for thousands of genes, microarray tech...
Microarrays are one of the latest breakthroughs in experimental molecular biology, which provide a p...
Sawannee Sutheeworapong,1,2 Motonori Ota,4 Hiroyuki Ohta,1 Kengo Kinoshita2,31Department of Biologic...