Bioinformatics in proteomics: application, terminology, and pitfalls

Pathology, Research and Practice
Jan C Wiemer, Alexander Prokudin

Abstract

Bioinformatics applies data mining, i.e., modern computer-based statistics, to biomedical data. It leverages on machine learning approaches, such as artificial neural networks, decision trees and clustering algorithms, and is ideally suited for handling huge data amounts. In this article, we review the analysis of mass spectrometry data in proteomics, starting with common pre-processing steps and using single decision trees and decision tree ensembles for classification. Special emphasis is put on the pitfall of overfitting, i.e., of generating too complex single decision trees. Finally, we discuss the pros and cons of the two different decision tree usages.

References

Nov 29, 2002·Archives of Pathology & Laboratory Medicine·Alex J RaiDaniel W Chan
Dec 15, 2004·Journal of Proteome Research·Matthias P A EbertChristoph Röcken

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Citations

Mar 29, 2011·Critical Care Clinics·Brian CasserlyMitchell M Levy
Feb 25, 2006·Mass Spectrometry Reviews·Andrew K OttensNancy D Denslow
Jul 25, 2021·International Journal of Molecular Sciences·Monokesh K SenJens R Coorssen

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