Sentence examples for microarray data well from inspiring English sources

This model matches the microarray data well but it is not satisfying enough in revealing more biological sense.

Four primers (BTG2, EGR1, FOS, and ATF3) validated the microarray data well (rates were 67 100%), and two primers, CLU and AHNAK, did not validate (rates were 33 and 17%, respectively).

We present a quality control analysis of the normalized microarray data, as well as overviews for differentially regulated genes.

Our microarray data correlated well with the qRT-PCR results.

Efficient computational analysis of microarray data as well as the discovery of putative associations between transcription factors and DNA binding sites are issues of prominent importance in bioinformatics.

We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies.

Full analysis of microarray data as well as raw data can be found on http://www.ebi.ac.uk/miamexpress (accession number is E-MEXP-1234, public access is available from the 10.11.2007).

We have developed a dynamical model for lineage determination: stem cell, trophectoderm and endoderm, for a network whose components are extracted from ChiP-on-chip and microarray data as well as literature studies [9].

This study offers a reliable benchmark data set that may be used for the comparison of 1) competing exon expression measures, 2) the variety of methods suitable for mapping exon array measures to the wealth of previously generated microarray data as well as 3) the development of more advanced exon- and transcript-level expression summarization methods.

In addition, this study offers a reliable benchmark data set for the comparison of competing exon expression measures, the selection of methods suitable for mapping exon array measures to the wealth of previously generated microarray data, as well as the development of more advanced methods for exon- and transcript-level expression summarization.

We tested the effect of using 20, 50, and 200 ranked marker genes from a list of differentially expressed genes, as they represent the number of markers commonly used in classification and prediction studies for microarray data, as well as the level at which we might expect to begin to see noise.