Sentence examples for extraction of biological from inspiring English sources
One role of bioinformatics is to assist biologists in the extraction of biological knowledge from this data flood.
Here, we develop a mathematical framework that defines this tradeoff between mRNA-sequencing depth and error in the extraction of biological information.
Outliers, extreme deviations from the respective fraction means, were detected by a boxplot approach and replaced with the corresponding fraction mean to promote extraction of biological relevant and robust information (Text S1).
Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning.
The statistical values were mapped and the reporter algorithm [4] was applied using three different types of biological networks derived from Gene Ontologies (GO) [43], a genome-scale metabolic model (GSMM) of Mus musculus [15] and biological evidences from the Reactome database [16], for extraction of biological responses according to influence of different diets on each specific tissue.
This approach requires the extraction of biological data sets from a wide range of sources.
Chemometrics is becoming an essential part of systems biology studies owing to its ability to facilitate extraction of relevant biological information from the huge amount of data produced by-omics technologies.
Analyses of small RNAs such as miRNAs and siRNAs typically start with an organic extraction of the biological sample, often using commercially available reagents such as Trizol (Invitrogen).
For example, in HIV infection, the analysis of the decay of circulating virus in the blood directly after treatment with anti-viral drugs allows the extraction of important biological parameters such as infected cell lifetime and the half life of free virus [24].
Literature mining allows the extraction of meaningful biological information from publications to generate networks [ 6].
One of the inherent challenges in transcriptome profiling is the effective analysis of large-scale datasets to optimize extraction of relevant biological meaning.
