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How to distinguish healthy from diseased? Classification strategy for mass spectrometry‐based clinical proteomics
Author(s) -
Hendriks Margriet M. W. B.,
Smit Suzanne,
Akkermans Wies L. M. W.,
Reijmers Theo H.,
Eilers Paul H. C.,
Hoefsloot Huub C. J.,
Rubingh Carina M.,
de Koster Chris G.,
Aerts Johannes M.,
Smilde Age K.
Publication year - 2007
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200700046
Subject(s) - proteomics , computer science , set (abstract data type) , data science , artificial intelligence , machine learning , computational biology , bioinformatics , data mining , biology , biochemistry , gene , programming language
SELDI‐TOF‐MS is rapidly gaining popularity as a screening tool for clinical applications of proteomics. Application of adequate statistical techniques in all the stages from measurement to information is obligatory. One of the statistical methods often used in proteomics is classification: the assignment of subjects to discrete categories, for example healthy or diseased. Lately, many new classification methods have been developed, often specifically for the analysis of X‐omics data. For proteomics studies a good strategy for evaluating classification results is of prime importance, because usually the number of objects will be small and it would be wasteful to set aside part of these as a ‘mere’ test set. The present paper offers such a strategy in the form of a protocol which can be used for choosing among different statistical classification methods and obtaining figures of merit of their performance. This paper also illustrates the usefulness of proteomics in a clinical setting, serum samples from Gaucher disease patients, when used in combination with an appropriate classification method.