Sentence examples for molecular variables such from inspiring English sources

Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding microRNA and mRNA expression profiles.

It is likely that the most optimal approach to prediction of response to cytotoxic therapy will involve a combination of gene expression data, microRNA profiles, and molecular variables such as ER, HER2, and PR status.

Because molecular variables such as ER, PR, and HER2 status, as well as Topoisomerase IIA expression, have been shown to have an effect on chemotherapy sensitivity[17], [18], we tested the effect of these molecular variables on the ability of gene signatures to accurately identify patients sensitive to TFAC therapy.

A combined, prognostic score, including clinical variables, such as nodal status and perhaps tumor size, and molecular variables, such as optimized B-cell and IL-8 metagenes (measured by an RT-PCR or array-based method), may identify patients with very low risk of recurrence even with ER-, PgR- and HER2-negative breast cancer.

Consistent with this possibility, it is now recognised that breast cancers such as IDC and ILC evolve via multiple pathways involving different combinations of molecular variables such as TP53 gene mutations (commoner in IDC than ILC; see above) and/or mTOR pathway activation (commoner in ILC than IDC; see below).

The variables such as molecular structure of the pore surface and connectivity are ignored.

The effect of several variables such as molecular weight of PEG, tie line length and phase volume ratio on zymogen partitioning was explored.

The paper was characterised in terms of variables such as molecular weight, DP, pH, fibre furnish and other properties, using traditional analytical techniques, such as viscometry for DP and cold extraction for pH [19].

Furthermore, given that the data regarding HES varies with variables such as molecular weight in ways that is not consistent with any particular hypothesis, this suggests that other confounding factors may exist that are not being accounted for.

The evolution of variables, such as molecular concentrations, is represented using ordinary differential equations and cellular behaviour using the agent-based framework.

To investigate the simultaneous influence on survival, variables such as molecular-based subgrouping (luminal-like A vs luminal-like B vs basal-like), stage (0 vs I vs II), grade (1 vs 2 vs 3) and histotype (simple vs complex/mixed) were also examined by multivariate regression with the proportional hazard Cox regression model for censored data.