Sentence examples for various microarray data from inspiring English sources

The method for mixture distribution model is suitable to various microarray data types obtained from single noise sources, or from multiple noise sources.

We gratefully acknowledge the Citrus research community for the provision of various microarray data in the public domain.

Spice successfully identified cancer-related genes from various microarray data sets and found enzymes or COGs associated with biohydrogen production and motility phenotype by microbial organisms.

We gratefully acknowledge the grapevine research community for the provision of various microarray data in the public domain and the anonymous referees for providing us with constructive comments and suggestions.

It would be helpful if the authors mention how their findings can help molecular biologists to make inferences about gene expression data of various microarray data sets and their biological implications.

Spice improved identification of cancer-related groups of genes from various microarray data sets and detected groups of genes associated with microbial biohydrogen production and motility, many of which were reported in literature.

Here, we extend the analysis with various other microarray data including those of several plant hormone and stress responses.

June 8-9, 2006 ~ North Carolina, U.S.A. CAMDA 2006 offers researchers from computer science, statistics, molecular biology, and other areas an opportunity to benefit from the critical evaluation of various techniques in microarray data analyses.

First, when comparing HSkMCs, ETV2-HSkMCs and independent various endothelial cells microarray data (Aranguren et al., 2013), heatmap analyses showed that ETV2-HSkMCs were closer to endothelial cells than HSkMCs (Fig. S4).

Various methods for microarray data analysis have been proposed to address this problem.

This tool was developed for large-scale gene expression cluster analysis and integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines [ 12].