The quantitative comparison of two or more microarrays can reveal, for example, the distinct patterns of gene expression that define different cellular phenotypes or the genes that are induced in the cellular response to certain stimulations. Normalization of the measured intensities is a prerequisite of such comparisons. However, a fundamental problem in cDNA microarray analysis is the lack of a common standard to compare the expression levels of different samples. Several normalization protocols have been proposed to overcome the variabilities inherent in this technology. We have developed a normalization procedure based on within-array replications via a semilinear in-slide model, which adjusts objectively experimental variations without...
Profiling miRNA levels in cells with miRNA microarrays is becoming a widely used technique. Although...
For cDNA microarray, the major sources of fluctuations can be listed according to the processes by w...
UnrestrictedThis thesis consists of three parts, reflecting three levels of Microarray data analysis...
Background The normalization of DNA microarrays allows comparison among samples by adjusting for ...
A Semilinear In-Slide Model Is Introduced To Remove The Intensity Effect In The Scanning Process. It...
DNA microarray technologies have the capability of simultaneously measuring the abundance of thousan...
Abstract Background DNA microarray technology provides a powerful tool for characterizing gene expre...
The recent development of complementary DNA microarray technology pro-vides a powerful analytical to...
When using cDNA microarrays, normalization to correct labeling bias is a common preliminary step bef...
Motivation: A major challenge in utilizing microarray technologies to measure nucleic acid abundance...
Introduction: Numerous methods exist for basic processing, e.g. normalization, of microarray gene ex...
This paper investigates subset normalization to adjust for location biases (e.g., splotches) combine...
[[abstract]]This paper investigates subset normalization to adjust for location biases (e.g., splotc...
Microarrays allow researchers to measure the expression of thousands of genes in a single experiment...
Microarray technology has been used as a routine high-throughput tool in biological research to char...
Profiling miRNA levels in cells with miRNA microarrays is becoming a widely used technique. Although...
For cDNA microarray, the major sources of fluctuations can be listed according to the processes by w...
UnrestrictedThis thesis consists of three parts, reflecting three levels of Microarray data analysis...
Background The normalization of DNA microarrays allows comparison among samples by adjusting for ...
A Semilinear In-Slide Model Is Introduced To Remove The Intensity Effect In The Scanning Process. It...
DNA microarray technologies have the capability of simultaneously measuring the abundance of thousan...
Abstract Background DNA microarray technology provides a powerful tool for characterizing gene expre...
The recent development of complementary DNA microarray technology pro-vides a powerful analytical to...
When using cDNA microarrays, normalization to correct labeling bias is a common preliminary step bef...
Motivation: A major challenge in utilizing microarray technologies to measure nucleic acid abundance...
Introduction: Numerous methods exist for basic processing, e.g. normalization, of microarray gene ex...
This paper investigates subset normalization to adjust for location biases (e.g., splotches) combine...
[[abstract]]This paper investigates subset normalization to adjust for location biases (e.g., splotc...
Microarrays allow researchers to measure the expression of thousands of genes in a single experiment...
Microarray technology has been used as a routine high-throughput tool in biological research to char...
Profiling miRNA levels in cells with miRNA microarrays is becoming a widely used technique. Although...
For cDNA microarray, the major sources of fluctuations can be listed according to the processes by w...
UnrestrictedThis thesis consists of three parts, reflecting three levels of Microarray data analysis...