Literature DB >> 19120032

Breast cancer diagnosis from proteomic mass spectrometry data: a comparative evaluation.

David J Hand1.   

Abstract

The performance results of a wide range of different classifiers applied to proteomic mass spectra data, in a blind comparative assessment organised by Bart Mertens, are reviewed. The different approaches are summarised, issues of how to evaluate and compare the predictions are described, and the results of the different methods are examined. Although the different methods perform differently, their rank ordering varies according to how one measures performance, so that one cannot draw unequivocal conclusions about which is 'best.' Instead, it is clear that what matters is not the method by itself, but the interaction of method and user - the degree of sophistication of the user with a method. Nevertheless, such competitions do serve the useful role of setting (constantly improving) baselines against which new researchers can pit their wits and methods, as well as providing standards against which new methods should be assessed.

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Year:  2008        PMID: 19120032     DOI: 10.2202/1544-6115.1435

Source DB:  PubMed          Journal:  Stat Appl Genet Mol Biol        ISSN: 1544-6115


  9 in total

1.  Combination approaches improve predictive performance of diagnostic rules for mass-spectrometry proteomic data.

Authors:  Alexia Kakourou; Werner Vach; Bart Mertens
Journal:  J Comput Biol       Date:  2014-12       Impact factor: 1.479

2.  A comparison of methods for classifying clinical samples based on proteomics data: a case study for statistical and machine learning approaches.

Authors:  Dayle L Sampson; Tony J Parker; Zee Upton; Cameron P Hurst
Journal:  PLoS One       Date:  2011-09-28       Impact factor: 3.240

3.  An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data.

Authors:  Susmita Datta; Vasyl Pihur; Somnath Datta
Journal:  BMC Bioinformatics       Date:  2010-08-18       Impact factor: 3.169

4.  A note on Youden's J and its cost ratio.

Authors:  Niels Smits
Journal:  BMC Med Res Methodol       Date:  2010-09-30       Impact factor: 4.615

5.  Toward digital staining using imaging mass spectrometry and random forests.

Authors:  Michael Hanselmann; Ullrich Köthe; Marc Kirchner; Bernhard Y Renard; Erika R Amstalden; Kristine Glunde; Ron M A Heeren; Fred A Hamprecht
Journal:  J Proteome Res       Date:  2009-07       Impact factor: 4.466

6.  A new way of identifying biomarkers in biomedical basic-research studies.

Authors:  Alexander Yassouridis; Tonia Ludwig; Axel Steiger; Friedrich Leisch
Journal:  PLoS One       Date:  2012-05-11       Impact factor: 3.240

7.  Boosting for high-dimensional two-class prediction.

Authors:  Rok Blagus; Lara Lusa
Journal:  BMC Bioinformatics       Date:  2015-09-21       Impact factor: 3.169

Review 8.  Open source libraries and frameworks for biological data visualisation: a guide for developers.

Authors:  Rui Wang; Yasset Perez-Riverol; Henning Hermjakob; Juan Antonio Vizcaíno
Journal:  Proteomics       Date:  2015-02-05       Impact factor: 3.984

9.  Getting started in computational mass spectrometry-based proteomics.

Authors:  Olga Vitek
Journal:  PLoS Comput Biol       Date:  2009-05-29       Impact factor: 4.475

  9 in total

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