Developing a Discrimination Rule between Breast Cancer Patients and Controls Using Proteomics Mass Spectrometric Data: A Three-Step Approach

Heidema A. Geert and Nagelkerke Nico
Additional contact information
Heidema A. Geert: Maastricht University; National Institute of Public Health and the Environment; Wageningen University and Research Centre
Nagelkerke Nico: United Arab Emirates University

Statistical Applications in Genetics and Molecular Biology, 2008, vol. 7, issue 2, 11

Abstract: To discriminate between breast cancer patients and controls, we used a three-step approach to obtain our decision rule. First, we ranked the mass/charge values using random forests, because it generates importance indices that take possible interactions into account. We observed that the top ranked variables consisted of highly correlated contiguous mass/charge values, which were grouped in the second step into new variables. Finally, these newly created variables were used as predictors to find a suitable discrimination rule. In this last step, we compared three different methods, namely Classification and Regression Tree (CART), logistic regression and penalized logistic regression. Logistic regression and penalized logistic regression performed equally well and both had a higher classification accuracy than CART. The model obtained with penalized logistic regression was chosen as we hypothesized that this model would provide a better classification accuracy in the validation set. The solution had a good performance on the training set with a classification accuracy of 86.3%, and a sensitivity and specificity of 86.8% and 85.7%, respectively.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1341 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:7:y:2008:i:2:n:5

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.2202/1544-6115.1341

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().