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Title: Extending Classification Algorithms to Case-Control Studies

Journal Article · · Biomedical Engineering and Computational Biology

Classification is a common technique applied to ’omics data to build predictive models and identify potential markers of biomedical outcomes. Despite the prevalence of case-control studies, the number of classification methods available to analyze data generated by such studies is extremely limited. Conditional logistic regression is the most commonly used technique, but the associated modeling assumptions limit its ability to identify a large class of sufficiently complicated ’omic signatures. We propose a data preprocessing step which generalizes and makes any linear or nonlinear classification algorithm, even those typically not appropriate for matched design data, available to be used to model case-control data and identify relevant biomarkers in these study designs. We demonstrate on simulated case-control data that both the classification and variable selection accuracy of each method is improved after applying this processing step and that the proposed methods are comparable to or outperform existing variable selection methods. Finally, we demonstrate the impact of conditional classification algorithms on a large cohort study of children with islet autoimmunity.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
Contributing Organization:
TEDDY Study Group
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
1556893
Report Number(s):
PNNL-SA--135302; {"","Journal ID: ISSN 1179-5972"}
Journal Information:
Biomedical Engineering and Computational Biology, Journal Name: Biomedical Engineering and Computational Biology Vol. 10; ISSN 1179-5972
Publisher:
SAGECopyright Statement
Country of Publication:
United States
Language:
English

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