Stability of Feature Selection in Multi-Omics Data Analysis
Artykuł w czasopiśmie
MNiSW
100
Lista 2024
| Status: | |
| Autorzy: | Łukaszuk Tomasz , Krawczuk Jerzy , Żyła Kamil, Kęsik Jacek |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2024 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 23 |
| Wolumen/Tom: | 14 |
| Numer artykułu: | 11103 |
| Strony: | 1 - 16 |
| Impact Factor: | 2,5 |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 28 listopada 2024 |
| Abstrakty: | angielski |
| In the rapidly evolving field of multi-omics data analysis, understanding the stability of feature selection is critical for reliable biomarker discovery and clinical applications. This study investigates the stability of feature-selection methods across various cancer types by utilizing 15 datasets from The Cancer Genome Atlas (TCGA). We employed classifiers with embedded feature selection, including Support Vector Machines (SVM), Logistic Regression (LR), and Lasso regression, each incorporating L1 regularization. Through a comprehensive evaluation using five-fold cross- validation, we measured feature-selection stability and assessed the accuracy of predictions regarding TP53 mutations, a known indicator of poor clinical outcomes in cancer patients. All three classifiers demonstrated optimal feature-selection stability, measured by the Nogueira metric, with higher regularization (fewer selected features), while lower regularization generally resulted in decreased stability across all omics layers. Our findings indicate differences in feature stability across the various omics layers; mirna consistently exhibited the highest stability across classifiers, while the mutation and rna layers were generally less stable, particularly with lower regularization. This work highlights the importance of careful feature selection and validation in high-dimensional datasets to enhance the robustness and reliability of multi-omics analyses. |
