Sentence examples for microarray data often from inspiring English sources

The high dimensional feature vectors in the microarray data often contain large number of features that are not useful in the process of classification.

In addition, microarray data often brings in multiple missing gene expression values and noisy signals from the experiments.

General titles about ontology-driven analysis of microarray data often obscure that a publication is concerned solely with gene annotations.

Control of the Type I error in microarray data often goes at the expense of the Type II error that remains uncontrolled and (too) large.

Our approach builds on previous studies showing that reference genes identified from microarray data often performed better in normalizing RT-qPCR experiments than commonly used reference genes.

Analyses of microarray data often begin with preprocessing to remove technical variation, such as signal intensity variation among different arrays or binding efficiency variation among different probes targeting the same gene.

Microarray data are often noisy, and consequently, the reproducibility often is low across datasets from different laboratories for the same disease.

We describe methods and software tools for doing data analysis based on Affymetrix microarray data, emphasizing often neglected issues.

Analysis of microarray data is often challenging due to the lack of a statistical model that is amenable to biological variation in a small number of samples.

Microarray data is often normalized globally, meaning that the distribution of expression levels of all genes is assumed to be constant in all samples.

Microarray data however often contains many missing values.