English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One important preprocessing step in the analysis of microarray data isbackground subtraction. In high-density oligonucleotide arrays this isrecognized as a crucial step for the global performance of the data analysisfrom raw intensities to expression values.  We propose here an algorithm for background estimation based on a model inwhich the cost function is quadratic in a set of fitting parameters such thatminimization can be performed through linear algebra. The model incorporatestwo effects: 1) Correlated intensities between neighboring features in the chipand 2) sequence-dependent affinities for non-specific hybridization fitted byan extended nearest-neighbor model.  The algorithm has been tested on 360 GeneChips from publicly available dataof recent expression experiments. The algorithm is fast and accurate. Strongcorrelations between the fitted values for different experiments as well asbetween the free-energy parameters and their counterparts in aqueous solutionindicate that the model captures a significant part of the underlying physicalchemistry.

Linear model for fast background subtraction in oligonucleotide microarrays