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1 Statistical measures of reliability and validity were used to evaluate microarray data .
2 The proposed method is also applied to a Glioblastoma microarray data analysis.
3 These results were confirmed with microarray data from an additional 988 samples.
4 This unit demonstrates how to prepare and analyze microarray data in GenePattern.
5 The methodology is illustrated by using microarray data from leukaemia patients.
6 We have developed programs to facilitate analysis of microarray data in Escherichia coli.
7 Semi-quantitative and real-time qRT-PCR analysis of some genes agrees with the microarray data .
8 Here, we analyzed microarray data of 181 early-stage lung cancer patients.
9 However, methodologies for biomarker discovery using microarray data often provide results with limited overlap.
10 Results: We present results on synthetic and real microarray data .
11 Normalization of expression levels applied to microarray data can help in reducing measurement error.
12 Results: Reliability of the microarray data from pooled samples was confirmed by real-time RT-PCR.
13 Hierarchic clustering of microarray data was used to correlate binding patterns with clinical findings.
14 Real microarray data obtained from HapMap samples are randomly partitioned to create different reference sets.
15 The proposed first-order correction method allows direct implementation in existing calibration algorithms for microarray data .
16 The qRT-PCR results correlated well with the microarray data .
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This collocation consists of: Microarray data across language varieties