<p>(A) Enumerating all biclusters exhaustively by mining maximal frequent itemsets. Insignificant biclusters are filtered out by empirical hypothesis testing. (B) Merging overlapping biclusters with the same experimental conditions if they keep significance. (C) Generating gene set networks from overlapping biclusters. (D) Analyzing gene functions by using the obtained networks.</p
BACKGROUND: Accumulated biological research outcomes show that biological functions do not depend on...
Biclustering or simultaneous clustering of both genes and conditions have generated considerable int...
In DNA microarray experiments, discovering groups of genes that share similar transcriptional charac...
Detecting biclusters from expression data is useful, since biclusters are coexpressed genes under on...
Biclustering extracts coexpressed genes under certain experimental conditions, providing more precis...
<p>(A) a heat map, (B) a line chart, (C) a matrix of expression values, and (D) a sample gene set ne...
Accumulated biological research outcomes show that biological functions do not depend on individual ...
<p>(A) Three overlapping biclusters: blue , orange and purple biclusters are overlapped with each ...
Abstract—The biclustering method can be a very useful analysis tool when some genes have multiple fu...
Aim of clustering of data is to analyze gene expression data. Recently, biclustering or simultaneous...
Biclustering or simultaneous clustering of both genes and conditions has generated considerable inte...
A good number of biclustering algorithms have been proposed for grouping gene expression data. Many ...
Summary: Besides classical clustering methods such as hierarchical cluste-ring, in recent years bicl...
Motivation: Biclustering has been emerged as a powerful tool for identification of a group of co-exp...
Analysis of large scale geonomics data, notably gene expression, has initially focused on clustering...
BACKGROUND: Accumulated biological research outcomes show that biological functions do not depend on...
Biclustering or simultaneous clustering of both genes and conditions have generated considerable int...
In DNA microarray experiments, discovering groups of genes that share similar transcriptional charac...
Detecting biclusters from expression data is useful, since biclusters are coexpressed genes under on...
Biclustering extracts coexpressed genes under certain experimental conditions, providing more precis...
<p>(A) a heat map, (B) a line chart, (C) a matrix of expression values, and (D) a sample gene set ne...
Accumulated biological research outcomes show that biological functions do not depend on individual ...
<p>(A) Three overlapping biclusters: blue , orange and purple biclusters are overlapped with each ...
Abstract—The biclustering method can be a very useful analysis tool when some genes have multiple fu...
Aim of clustering of data is to analyze gene expression data. Recently, biclustering or simultaneous...
Biclustering or simultaneous clustering of both genes and conditions has generated considerable inte...
A good number of biclustering algorithms have been proposed for grouping gene expression data. Many ...
Summary: Besides classical clustering methods such as hierarchical cluste-ring, in recent years bicl...
Motivation: Biclustering has been emerged as a powerful tool for identification of a group of co-exp...
Analysis of large scale geonomics data, notably gene expression, has initially focused on clustering...
BACKGROUND: Accumulated biological research outcomes show that biological functions do not depend on...
Biclustering or simultaneous clustering of both genes and conditions have generated considerable int...
In DNA microarray experiments, discovering groups of genes that share similar transcriptional charac...