Document Type
Article
Publication Date
2026
DOI
10.1002/pst.70097
Publication Title
Pharmaceutical Statistics
Volume
25
Issue
3
Pages
e70097
Abstract
Analysis of genomics data for predicting disease outcomes is a fast-growing field in medical research. There often exist categorical, specifically, ordinal outcomes that need to be predicted based on genomic profiles. This has led to recent development of some high-dimensional ordinal classification methods that can address the large dimensionality of the genomic covariate set. These high-dimensional ordinal models tend to vary widely in their performance depending on the data they are applied to and the evaluation criteria used. In this article, we outline an ensemble ordinal classifier that integrates different ordinal modeling approaches through bootstrap-based model evaluation, multi-metric performance assessment, and rank aggregation to produce a final prediction that can alleviate the uncertainty of relying on a single model. Through multiple simulated studies and real genomic data analyses, we show that the ensemble method consistently ranks among the top-performing models. These findings underscore the potential of ensemble learning to improve the robustness and predictive accuracy of high-dimensional ordinal classification in genomic research.
Rights
© 2026 The Authors.
This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Data Availability
Article states: "The data that support the findings of this study are available in Gene Expression Omnibus database at https://www.ncbi.nlm.nih.gov/gds. These data were derived from the following resources available in the public domain: GSE22216: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE22216; GSE3365: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3365."
Original Publication Citation
Rathnasekara, H. K., & Sikdar, S. (2026). An ensemble classifier for ordinal outcomes in high-dimensional genomics data. Pharmaceutical Statistics, 25(3), Article e70097. https://doi.org/10.1002/pst.70097
ORCID
0009-0003-2182-3532 (Rathnasekara), 0000-0003-1230-5162 (Sikdar)
Repository Citation
Rathnasekara, Heranga K. and Sikdar, Sinjini, "An Ensemble Classifier for Ordinal Outcomes in High-Dimensional Genomics Data" (2026). Mathematics & Statistics Faculty Publications. 335.
https://digitalcommons.odu.edu/mathstat_fac_pubs/335
Supporting Information
Included in
Data Science Commons, Genomics Commons, Medical Genetics Commons, Statistics and Probability Commons