Sentence examples for microarray data integration from inspiring English sources

Unlike the past work on database interoperation in the bioinformatics community, this database design will take into account the important issues in microarray data integration including a lack of a common shared microarray-ontology and having dynamic data representation of the microarray data sources.

We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models.

Here, we describe a novel statistical method for identifying genes with outlier expression in large-scale microarray data integration studies and compare this method with existing algorithms.

More microRNA specific normalization methods are in demand for cross-platform microRNA microarray data integration and comparison, which will improve the reproducibility and consistency between platforms.

Besides the microarray data integration, we also have applied the principle to the clustering problem and have introduced a novel clustering framework, SignatureClust [18].

We also have developed microarray data integration techniques that exploit inter-gene relations, such as correlation signature [16] and signature cube [17].

MvE was responsible for generation and analysis of the microarray data, performed data integration and interpretation and drafted the manuscript; EV and SW were responsible for metabolomics measurements and metabolomics data analysis; TP and JN were responsible for oxylipids measurements; CR and AS performed the multivariate analysis.

AS analyzed all existing systems biology datasets, new RosR microarray data, and performed data integration and interpretation.

Other related previous methods include, among others, probabilistic methods for combining sequences and microarray data [51], [52], general frameworks for data integration (see [53]), and a supervised method for binding site prediction [54], [55].

Through miRNA microarray and protein interaction network data integration, our approach presented here could not only pick out key miRNAs involved in MI but also bring novel insight in the pathological mechanisms of MI from the miRNA aspect.

AR carried out stress treatment and sample collection, design and execution of microarray, transcriptome sequencing, QGE analysis, data integration, and drafted the manuscript.