Sentence examples for microarray data clustering from inspiring English sources

The dendrogram is one of the traditional approaches to perform microarray data clustering.

With Biolisp, among others, one can perform simple biological natural language processing on PubMed, work on sequences, represent and search graphs, produce trees and perform microarray data clustering.

In this paper, we propose a clustering algorithm called M-CLUBS (for Microarray data CLustering Using Binary Splitting) exhibiting higher accuracy than the hierarchical ones proposed so far while allowing a faster computation with respect to partition based approaches.

For the microarray data, clustering and statistical analysis were performed using SUMO and Genego (metacore) software packages.

However, more appropriate distance measures accounting for the discreet, Poisson-distributed structure of SAGE data have been shown to produce better clustering results than those achieved with conventional Euclidian or Pearson similarity measures routinely used in microarray data clustering [12].

Most of the applications discussed in the last section mainly applied tools currently available in microarray data clustering tools such as TIGR MeV [ 35] and GeneSpring [ 42].

PCA confirmed that the microarray data clustered by sex and by treatment type suggesting that there are significant differences at the level of gene expression between i) male and female pups and ii) control, hyper, hypo and hypo+ pups.

After a careful re-analysis of microarray data, clusters of co-expressed genes were identified in each data set.

Although widely used for the analysis of gene expression microarray data, cluster analysis may not be the most appropriate statistical technique for some study aims.

For microarray data analysis, clustering techniques is a popular technique for mining significant biological models.

We validated the microarray data by clustering the processed raw data based on all available App/ Aplp2 probe sets.