Sentence examples for diverse biological samples from inspiring English sources

Tangible examples include system-wide glycoproteomics studies detecting thousands of intact glycopeptides from hundreds of glycoproteins from diverse biological samples.

Second, signal detection accuracy depends strongly on expression level, even in datasets of diverse biological samples with variable background and small gene expression ranges.

Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples.

To our knowledge, no study has made a systematic examination of microarray signal detection accuracy and precision with diverse biological samples in reference to an amplification-free, digital RNA measurement.

Our analyses provide a better understanding of how preprocessed microarray results reflect RNA levels in diverse biological samples, translating microarray expression value differences into molecular changes between samples, and highlighting tissue-specific noise properties.

The basic idea is that the frozen parameter estimates are created from gene expression array datasets on diverse biological samples from a range of tissues and, therefore, capture the vast heterogeneity within and between samples/arrays.

Future work will systematically evaluate the lifetime of SPE columns, LC columns, and the system as a whole, under different SPE and LC conditions for diverse analytes from various biological samples including plasma, serum, and whole blood samples.

This could open diverse applications of imprinted polymers for the extraction of compounds from various biological samples.

Metabolomics also has the most diverse range of instruments used to capture the biological metabolome matrix for a diverse range of biological samples in comparison to other -omics, with each technology requiring a wide range of parameters to be controlled and reported.

The general error model provides an advantage in building cell-type specific models in diverse but related biological samples.

We address the cell-type prediction challenge as a multilabel classification problem with hierarchical constraints to account for the diverse nature of biological samples.