Abstract:
: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.
journal_name
Proteomicsjournal_title
Proteomicsauthors
Hendriks MM,Smit S,Akkermans WL,Reijmers TH,Eilers PH,Hoefsloot HC,Rubingh CM,de Koster CG,Aerts JM,Smilde AKdoi
10.1002/pmic.200700046subject
Has Abstractpub_date
2007-10-01 00:00:00pages
3672-80issue
20eissn
1615-9853issn
1615-9861journal_volume
7pub_type
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