ArticleStatistical methods in medical research2022
ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data.
Article in Statistical methods in medical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Challenges and Opportunities for Statistics in Omics Data Analysis.Advances in experimental medicine and biology · 2026Review
- PAM clustering algorithm based on mutual information matrix for ATR-FTIR spectral feature selection and disease diagnosis.BMC medical research methodology · 2025Article
- A New Breast Cancer Discovery Strategy: A Combined Outlier Rejection Technique and an Ensemble Classification Method.Bioengineering (Basel, Switzerland) · 2024Article
- Robust double machine learning model with application to omics data.BMC bioinformatics · 2024Article
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4 authors.
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Abstract
The extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy.Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods.We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue samples. The pre-processed dataset consists of
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