Add to ORKGA DNA microarray is a biological tool to measure the activity of genes in different samples. These samples are, for example, coming from a tumor. A DNA microarray can measure simultaneously the activity for M genes in N samples, where M is approximately 20.000 and N is approximately 20. The results can be given as an MxN matrix, the expression matrix E. Sometimes elements in this matrix are missing due to various factors, for example dust on the chip or machine error. In this thesis we describe several methods to estimate the missing values. We compare these methods using some DNA microarray (online available).
Most of the gene expression data analysis algorithms require the entire gene expression matrix witho...
Motivation: Significance analysis of differential expression in DNA microarray data is an important ...
DNA microarrays are powerful tools for functional genomics studies. Each array contains thousands o...
DNA microarray is a high throughput gene profiling technology employed in numerous biological and me...
AbstractWe suggest here a new method of the estimation of missing entries in a gene expression matri...
Microarray gene expression data provide life science researchers with much more sensitive and detail...
Amongst the high-throughput technologies, DNA microarray experiments provide enormous quantity of ge...
Microarray experiments generate data sets with information on the expression levels of thousands of ...
DNA microarray experiment inevitably generates gene expression data with missing values. An importan...
and other research outputs A collateral missing value estimation algorithm for DNA microarray
Abstract: DNA microarrays are an emerging technology for genome-wide analysis of gene expression. Su...
Gene expression microarray data often include multiple missing values. Most gene expression analysis...
Motivation: Data from microarray experiments are usually in the form of large matrices of expression...
Abstract. Microarrays have unique ability to probe thousands of genes at a time that makes it a usef...
Missing value estimation is important in DNA microarray data analysis. A number of algorithms have b...
Most of the gene expression data analysis algorithms require the entire gene expression matrix witho...
Motivation: Significance analysis of differential expression in DNA microarray data is an important ...
DNA microarrays are powerful tools for functional genomics studies. Each array contains thousands o...
DNA microarray is a high throughput gene profiling technology employed in numerous biological and me...
AbstractWe suggest here a new method of the estimation of missing entries in a gene expression matri...
Microarray gene expression data provide life science researchers with much more sensitive and detail...
Amongst the high-throughput technologies, DNA microarray experiments provide enormous quantity of ge...
Microarray experiments generate data sets with information on the expression levels of thousands of ...
DNA microarray experiment inevitably generates gene expression data with missing values. An importan...
and other research outputs A collateral missing value estimation algorithm for DNA microarray
Abstract: DNA microarrays are an emerging technology for genome-wide analysis of gene expression. Su...
Gene expression microarray data often include multiple missing values. Most gene expression analysis...
Motivation: Data from microarray experiments are usually in the form of large matrices of expression...
Abstract. Microarrays have unique ability to probe thousands of genes at a time that makes it a usef...
Missing value estimation is important in DNA microarray data analysis. A number of algorithms have b...
Most of the gene expression data analysis algorithms require the entire gene expression matrix witho...
Motivation: Significance analysis of differential expression in DNA microarray data is an important ...
DNA microarrays are powerful tools for functional genomics studies. Each array contains thousands o...