Sentence examples for microarray data with from inspiring English sources

Our study provides an approach to analyze cDNA microarray data with defined false positive rates.

Dugas M, Schoch C, Schnittger S, Haferlach T, Danhauser-Riedl S, Hiddemann W, et al. A comprehensive leukemia database: integration of cytogenetics, molecular genetics and microarray data with clinical information, cytomorphology and immunophenotyping.

The described method allows us to better utilize the information available from microarray data with survival outcomes.

We normalized whole microarray data with the Affy package encoded using the R language (Gautier et al. 2004) and sorted by subspecies.

Butte et al. [70], in their 2011 editorial, discuss several Translational Bioinformatics studies featured in JAMIA which combine biological data (e.g. microarray data) with medical records to achieve medical gains as more data angles are tested in tandem.

We first confirmed the microarray data with quantitative PCR.

Next, DEGs were selected in real microarray data with those methods.

Additionally, SWang can uniquely detect significant genes in real microarray data with imperceptible differential expression but higher variety in kurtosis and skewness.

We validated microarray data with respect to direction of change of expression (up or down) for 293T (Figure 3A), KR4 (Figure 3B) and for C33A cells (Figure 3C).

We originally use a similar method to infer activity changes of transcription factors by integrating microarray data with ChIP-chip data or motif discovery data [44].

There are a number of accepted normalization techniques that can be applied to microarray data, with varying levels of complexity and transparency.