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Here, we proposed the integrated information management architecture for microarray experimental data.
When dealing with DNA microarray experimental data for example, the goal of biclustering algorithms is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition.
Analysis of microarray experimental data available for SCN (H.
In the post-genomics area, there is a sea of biological data including microarray experimental data.
Large amounts of microarray experimental data are available in public repositories.
All microarray experimental data are MIAME compliant and have been deposited in Gene Expression Omnibus (GEO) with the accession number: GSE69515 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi acc=GSE69515).nih.gov/geo/query/acc.cgi acc=GSE69515
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The microarray experimental plan and data analysis in this study are in accordance with the MIAME (minimum information about a microarray experiment) guidelines [ 36].
We used our method to characterize the detection performance of the four genomic microarray platforms using experimental data from 13 samples with submicroscopic copy-number variations hybridized to the different platforms.
We used TaqMan RT-PCR arrays as a reference to evaluate the accuracy of expression values from Affymetrix microarrays in two experimental data sets: one comprising 84 genes in 36 colon biopsies, and the other comprising 75 genes in 29 cancer cell lines.
To examine microarray accuracy using real experimental data, we calculated the signal detection slope of microarray expression values versus log-transformed, control-gene-normalized nCounter measurements by fitting linear regressions to the paired platform measurements of each gene.
Thus, both the relationship between the genes (i.e., their strength) determined by their topology and experimental data (microarrays and GWAS data) are used to analyze this pathway.
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