Add to ORKGA method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expression. To extract useful information from expression profiles, computational tools, for example, hierarchical clustering and self-orgnizing map (SOM) clustering, have been widely used to cluster and display data. Compared with hierarchica
The Problem: The goal of this research project is to identify genes belonging to the same functional...
<p>Hierarchical clustering was used to analyze the gene expression data by arranging the samples int...
AbstractWe propose a novel co-clustering algorithm that is based on self-organizing maps (SOMs). The...
This paper introduces a comprehensive review of a Growing Hierarchical Self-Organizing Map (GHSOM) r...
AbstractDNA microarray technologies together with rapidly increasing genomic sequence information is...
cDNA microarrays permit massively parallel gene expression analysis and have spawned a new paradigm ...
Modern high-throughput technologies such as microarrays, next generation sequencing and mass spectro...
DNA microarrays and cell cycle synchronization experiments have made possible the study of the mecha...
DNA microarrays and cell cycle synchronization experiments have made possible the study of the mecha...
DNA microarrays and cell cycle synchronization experiments have made possible the study of the mecha...
* to whom correspondence should be addressed Background: Self organizing maps (SOM) enable the strai...
Cluster analysis is one of the crucial steps in gene expression pattern (GEP) analysis. It leads to ...
Array technologies have made it straightforward to monitor simultaneously the expression pattern of ...
Background: 
Self organizing maps (SOM) enable the straightforward portraying of high-dimen...
Background: The availability of parallel, high-throughput microarray and sequencing experiments pose...
The Problem: The goal of this research project is to identify genes belonging to the same functional...
<p>Hierarchical clustering was used to analyze the gene expression data by arranging the samples int...
AbstractWe propose a novel co-clustering algorithm that is based on self-organizing maps (SOMs). The...
This paper introduces a comprehensive review of a Growing Hierarchical Self-Organizing Map (GHSOM) r...
AbstractDNA microarray technologies together with rapidly increasing genomic sequence information is...
cDNA microarrays permit massively parallel gene expression analysis and have spawned a new paradigm ...
Modern high-throughput technologies such as microarrays, next generation sequencing and mass spectro...
DNA microarrays and cell cycle synchronization experiments have made possible the study of the mecha...
DNA microarrays and cell cycle synchronization experiments have made possible the study of the mecha...
DNA microarrays and cell cycle synchronization experiments have made possible the study of the mecha...
* to whom correspondence should be addressed Background: Self organizing maps (SOM) enable the strai...
Cluster analysis is one of the crucial steps in gene expression pattern (GEP) analysis. It leads to ...
Array technologies have made it straightforward to monitor simultaneously the expression pattern of ...
Background: 
Self organizing maps (SOM) enable the straightforward portraying of high-dimen...
Background: The availability of parallel, high-throughput microarray and sequencing experiments pose...
The Problem: The goal of this research project is to identify genes belonging to the same functional...
<p>Hierarchical clustering was used to analyze the gene expression data by arranging the samples int...
AbstractWe propose a novel co-clustering algorithm that is based on self-organizing maps (SOMs). The...