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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background: High-throughput proteomics techniques, such as mass spectrometry(MS)-based approaches, produce very high-dimensional data-sets. In a clinicalsetting one is often interested in how mass spectra differ between patients ofdifferent classes, for example spectra from healthy patients vs. spectra frompatients having a particular disease. Machine learning algorithms are needed to(a) identify these discriminating features and (b) classify unknown spectrabased on this feature set. Since the acquired data is usually noisy, thealgorithms should be robust against noise and outliers, while the identifiedfeature set should be as small as possible.  Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), basedon the theory of compressed sensing that allows us to identify a minimaldiscriminating set of features from mass spectrometry data-sets. We show (1)how our method performs on artificial and real-world data-sets, (2) that itsperformance is competitive with standard (and widely used) algorithms foranalyzing proteomics data, and (3) that it is robust against random andsystematic noise. We further demonstrate the applicability of our algorithm totwo previously published clinical data-sets.

bmc bioinformatics

Sparse Proteomics Analysis - A compressed sensing-based approach for  feature selection and classification of high-dimensional proteomics mass  spectrometry data