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Identifying potential biomarkers in LC‐MS data
Author(s) -
Daszykowski M.,
Wu W.,
Nicholls A.W.,
Ball R.J.,
Czekaj T.,
Walczak B.
Publication year - 2007
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1066
Subject(s) - chemometrics , partial least squares regression , monte carlo method , random variate , mass spectrometry , computer science , chromatography , data mining , chemistry , statistics , mathematics , machine learning , random variable
In this paper an application of the uninformative variable elimination–partial least squares (UVE‐PLS) method extended by the Monte Carlo approach for selection of possible biomarkers from the liquid chromatography coupled with mass spectrometry (LC‐MS) data is reported. The main challenge consists not in the chemometrics analysis of LC‐MS data, but in the data organization. However, as demonstrated in our study, the selected variables are similar regardless of the data organization strategy. The best results are obtained for the standard normal variate (SNV) transformed data. Copyright © 2007 John Wiley & Sons, Ltd.