Bicluster|Based Identification of Gene Sets Through Multivariate ...

Bicluster-Based Identification of Gene Sets Through Multivariate ...

... in gene expression data is a pattern displayed by a group of genes in a subset of samples. Hence, gene sets can be identified through biclustering. A single ...

Bicluster-Based Identification of Gene Sets Through Multivariate ...

Omics technologies are among the most exciting developments in biology and medicine in recent decades. They offer a whole new way of investigating a sample ...

Healthcare Biclustering-Based Prediction on Gene Expression Dataset

The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the ...

Biclustering for the comprehensive search of correlated gene ... - NCBI

BCCA is a Pearson correlation coefficient-based biclustering method that finds groups of genes showing a correlated expression pattern across a ...

Biclustering on expression data: A review - ScienceDirect.com

The algorithm is based on the dominant set approach of Pavan and Pelillo [25]. In order to find a bicluster, genes and conditions are iteratively sorted using ...

GeneSetCluster: a tool for summarizing and integrating gene-set ...

To cluster the gene-sets into groups based on the calculated distance, ClusterGeneSets allows for two different methodologies: kmeans clustering ...

An unsupervised gene selection method based on multivariate ...

The clustering algorithm partitions the gene set into different partitions based on their similarity/distance measure. After partitioning ...

Bi-correlation clustering algorithm for determining a set of co ...

Biclusters determined by BCCA also show highly enriched functional categories. Using different gene expression datasets, we demonstrate strength and superiority ...

"Development of Biclustering Techniques for Gene Expression Data ...

Genomescale identification of CEMs can be modeled and solved by biclustering, a twodimensional data mining technique that allows clustering of rows and columns ...

Query-based biclustering of gene expression data using ...

A bicluster is modeled by a set of independent Normal distributions, one for each array that was assigned to the bicluster. For each array, an ...

Identification of differentially expressed gene modules in ...

DESMOND performs network-constrained biclustering on gene expression data and identifies gene mod- ules—connected sets of genes up- or down- ...

A Biclustering Method to Discover Co-regulated Genes Using ...

Given a data matrix A, reference row rr , PCC threshold ρ and minimum number of columns γ, identify a set of biclusters B = (X, Y ) such that rr ∈ X , m ...

MCbiclust: Massive correlating biclusters for gene expression data ...

Upon identifying a bicluster seed with FindSeed, one of the next steps is to identify which genes not in your chosen gene set are also highly correlated to the ...

Discovering biclusters in gene expression data based on high ...

Moreover, most of these algorithms can only detect a restricted set of bicluster patterns. Results: In this paper, we present a novel geometric ...

Discovering Statistically Significant Biclusters in Gene Expression ...

Gene expression data is modeled using a bipartite graph whose two sides correspond to the set of conditions ( and the set of genes ) . An edge *,+.-%/10 ...

Discovering Statistically Significant Biclusters in Gene Expression ...

In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to ...

Finding multiple coherent biclusters in microarray data using ...

A multiobjective genetic biclustering technique is proposed here that optimizes these objectives simultaneously. A novel encoding scheme that uses variable ...

Multi‐Omics Factor Analysis—a framework for unsupervised ...

We present Multi‐Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi‐omics data sets. MOFA infers a ...

Identification of Yeast Transcriptional Regulation Networks Using ...

Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays) to sequence features ...

An evaluation study of biclusters visualization techniques of gene ...

Biclustering is a non-supervised data mining technique used to analyze gene expression data, it consists to classify subgroups of genes that ...