StackFeat: a convergent algorithm for optimal predictor selection in genomic data
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917434411188224 |
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| author | Yermekov, Akbar Herrera-Martí, D. A. |
| author_facet | Yermekov, Akbar Herrera-Martí, D. A. |
| contents | In high-dimensional genomic data, the curse of dimensionality (d >> n) and limited sampling make feature selection inherently unstable - a critical barrier to biomarker discovery. We introduce StackFeat, an iterative algorithm that accumulates two statistics across repeated cross-validation: signed coefficients (measuring effect strength and direction) and selection frequencies (estimating selection probability). Only features ranking highly by both criteria are retained. On a COVID-19 miRNA dataset (GSE240888), StackFeat identified a stable 5-miRNA signature from 332 features (98.5% reduction), achieving AUC 0.922, significantly outperforming the benchmark 9-gene set (AUC 0.907, p = 0.0016). The signature includes hsa-miR-150-5p, a marker implicated in both COVID-19 survival and Dengue infection. This dual-criterion approach provides convergence guarantees absent in single-criterion methods, enabling discovery of known biomarkers, novel candidates, and previously unknown relationships.
Keywords: marker selection, feature selection, bioinformatics, dimensionality reduction, robust algorithm, stacking, miRNA, COVID-19 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22887 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | StackFeat: a convergent algorithm for optimal predictor selection in genomic data Yermekov, Akbar Herrera-Martí, D. A. Other Quantitative Biology In high-dimensional genomic data, the curse of dimensionality (d >> n) and limited sampling make feature selection inherently unstable - a critical barrier to biomarker discovery. We introduce StackFeat, an iterative algorithm that accumulates two statistics across repeated cross-validation: signed coefficients (measuring effect strength and direction) and selection frequencies (estimating selection probability). Only features ranking highly by both criteria are retained. On a COVID-19 miRNA dataset (GSE240888), StackFeat identified a stable 5-miRNA signature from 332 features (98.5% reduction), achieving AUC 0.922, significantly outperforming the benchmark 9-gene set (AUC 0.907, p = 0.0016). The signature includes hsa-miR-150-5p, a marker implicated in both COVID-19 survival and Dengue infection. This dual-criterion approach provides convergence guarantees absent in single-criterion methods, enabling discovery of known biomarkers, novel candidates, and previously unknown relationships. Keywords: marker selection, feature selection, bioinformatics, dimensionality reduction, robust algorithm, stacking, miRNA, COVID-19 |
| title | StackFeat: a convergent algorithm for optimal predictor selection in genomic data |
| topic | Other Quantitative Biology |
| url | https://arxiv.org/abs/2604.22887 |