Sentence examples for molecular profiles generated from inspiring English sources

The molecular profiles generated provide opportunities to identify breast cancer cell lines with an altered copy number and expression of known cancer genes, useful to model pathogenesis and therapy, and to discovery new breast cancer genes.

In addition, the molecular profiles generated from FNAs of different breast tumors can be distinguished from one another.

Molecular profiles were generated for over 65% of all patients (improving as the trial proceeded), and these patients are representative of the entire data set.

The thermodynamic stability profiles generated here created distinct molecular markers to differentiate the three cell lines, and a significant fraction (∼45%) of the differentially stabilized proteins did not have altered expression levels.

In comparison, 8.8 (SD 4.4) T-RF bands were resolved in profiles generated by Culture-derived Molecular Analysis.

Examples of the ways in which T-RFLP profiles, generated either directly from sputum samples (Direct Molecular Analysis), or from cultures derived from those samples (Culture-derived Molecular Analysis), differ are shown in Figure 1.

Population differentiation among human and bovine meat isolates was assessed by using analysis of molecular variance (AMOVA) applied to haplotypes of isolates' PFGE profiles (generated in BioNumerics) using Arlequin software version 3.5.1.2 (http://cmpg.unibe.ch/software/arlequin3/).ch/software/arlequin3/

More broadly, it provides a template to use in providing such functionality for other molecular profile data generated by academic institutions, public projects, or the private sector.

As each cell line exhibits unique molecular genetic characteristics comparisons of chromosome 3 transcriptome modifications were made with respect to gene expression profiles generated with each growth condition within each experimental cell line model.

Molecular profiling of psoriatic phenotypes followed by classification of tissue samples into appropriate disease classes has the potential to derive clusters of similar transcription responses from the entire repertoire of profiles generated.

These researchers also showed that the molecular profiles of tumors generated by MMTV- tva/RCAS- PyMT, K6- tva/RCAS- PyMT, and MMTV- PyMT were not similar, with the K6- tva tumors having a unique profile similar to that of a normal-like breast cancer subtype [ 27].