Sentence examples for microarray time series from inspiring English sources

Normalisation analysis has been performed on four distinct, publicly available, raw datasets, representing microarray time series measurements during the Yeast Saccharomyces Cerevisiae cell cycle.

Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles.

Our microarray time series assays revealed that the expression of most iron acquisition genes rises dramatically in response to paraquat independently of the SoxRS pathway.

By integrating our microarray time series results with other microarray data, E. coli databases and the primary literature, we propose a model of the primary transcriptional response.

By integrating our microarray time series results with other microarray data, E. coli databases and the primary literature, we propose a model of the primary transcriptional response containing 226 protein-coding and sRNA sequences.

Microarray time series have been extensively generated to study periodic biological processes, such as the cell cycle [1], circadian regulation [2], [3], the life cycle of malaria parasite in human blood [4] and vertebrae segmentation [5].

Results reveal that the FNT model can improve the prediction accuracy of microarray time-series data effectively and reconstruct gene regulatory network accurately.

We first briefly explain the function of gene regulatory networks and DNA microarray time-series.

In particular, microarray time-series represent an important source of information about cellular dynamics.

Using the same ideas, another class of a networks that can be determined are gene regulatory networks given microarray time-series data.

Within the work, we propose a combinatorial theory-based learning pattern for the inference and analysis of genetic networks from microarray time-series datasets.