This study presents gene discovery from microarrays and the development and validation from a large qRT-PCR data set of a whole blood-derived, qRT-PCR based gene expression score for the assessment of smoking status.
Microarray-based gene expression assessment is a very useful method for prediction of diseases, tumor classification and drug responses.
We also obtained RNA-seq based gene expression data (RPKM) from64 and17 for mESC and pro-B cells, respectively.
Here, we used a DNA array-based gene expression profiling approach, together with assessment of the cytotoxic activity of several widely applied anti-cancer agents, in two collections of human lung cancer cell lines.
This study presents gene discovery from microarrays and development from a large RT-PCR data set of a whole blood derived RT-PCR based gene-expression algorithm for assessment of obstructive CAD likelihood in non-diabetic patients, which was subsequently validated in an independent patient set [ 6].
High-throughput expression profiling technologies have effectively altered the experimental design of gene expression assessments.
Based on gene expression profiling and immunohistochemical morphometric assessments, TNBCs, which account approximately for ∼13% of all breast cancers, have been suggested to be synonymous with basal-like tumors [ 1, 3– 6].
We have developed a whole blood classifier based on gene expression, age and sex for the assessment of obstructive CAD in non-diabetic patients from a combination of microarray and RT-PCR data derived from studies of patients clinically indicated for invasive angiography.
Xu, X. et al. High-fidelity CRISPR/Cas9- based gene-specific hydroxymethylation rescues gene expression and attenuates renal fibrosis.
This study was based on gene expression data from peripheral blood.
For each tumor, we identified normal immune subpopulations and malignant B cells based on gene expression.
