Looking inside self-organizing map ensembles with resampling and negative correlation learning

Scherbart, Alexandra; Nattkemper, Tim Wilhelm

doi:10.1016/j.neunet.2010.08.004

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…The PIP method shows a good correlation (r around 0.6) between predicted peptide peak heights and actual peptide quantities, but has yet to be applied to electrospray mass spectrometry. Another predictive method based on self organizing maps shows better performance [102]. Nevertheless, the limited accuracy of such predictive methods has been led researchers to prefer empirical data so far.…”

Section: Future Developmentmentioning

confidence: 99%

Review of software tools for design and analysis of large scale MRM proteomic datasets

Colangelo

Chung

Bruce

et al. 2013

Methods

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Selective or Multiple Reaction monitoring (SRM/MRM) is a liquid-chromatography (LC)/tandem-mass spectrometry (MS/MS) method that enables the quantitation of specific proteins in a sample by analyzing precursor ions and the fragment ions of their selected tryptic peptides. Instrumentation software has advanced to the point that thousands of transitions (pairs of primary and secondary m/z values) can be measured in a triple quadrupole instrument coupled to an LC, by a well-designed scheduling and selection of m/z windows. The design of a good MRM assay relies on the availability of peptide spectra from previous discovery-phase LC-MS/MS studies. The tedious aspect of manually developing and processing MRM assays involving thousands of transitions has spurred to development of software tools to automate this process. Software packages have been developed for project management, assay development, assay validation, data export, peak integration, quality assessment, and biostatistical analysis. No single tool provides a complete end-to-end solution, thus this article reviews the current state and discusses future directions of these software tools in order to enable researchers to combine these tools for a comprehensive targeted proteomics workflow.

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Section: Future Developmentmentioning

confidence: 99%