# Stability of Feature Selection in Multi-Omics Data Analysis
## 4 Discussion  
The aim of our work was not to precisely replicate the experiments presented in [PERSON] et al. [24] but to extend the evaluation metrics they used, focused on classification accuracy and related metrics, to include feature selection stability, which is increasingly recognized as an important evaluation parameter [22; 36; 51]. In our analysis, we used different feature-selection and classification methods than [PERSON] et al. [24] but applied them to the same datasets. We achieved similar classification accuracy values. Additionally, we demonstrated that when considering both classification accuracy and feature-selection stability, a model with different parameters can be identified as the best. Classification accuracy (or its derivative) remains a crucial parameter for evaluating the quality of a classification model, but incorporating feature-selection stability provides a more comprehensive assessment.  
In our analysis, the classification accuracy across individual omic layers and the combined feature set did not exhibit significant variations. This stability in accuracy may be attributed to the inherent biological nature of cancer, which manifests across multiple omics layers. The consistent representation of cancer-related pathways and processes in genomic, transcriptomic, proteomic, and metabolomic data suggests that key signals for classification are preserved across these diverse data types. This highlights the interconnectedness of biological systems in cancer pathology and reinforces the notion that integrating multi-omics data can offer a more comprehensive understanding of the disease.  
Furthermore, the robust performance across different omics layers underscores the potential of comprehensive multi-omics approaches for effective biomarker discovery. It also suggests that cancer characteristics are sufficiently prominent across all omics layers, resulting in similar predictive power. However, this consistency warrants further investigation into whether certain omics layers contribute disproportionately to classification outcomes. Such insights could inform future studies on optimal feature selection and model training in multi-omics research.  
Our future work could focus on extending the validation of the presented method to high-dimensional datasets beyond the medical domain, such as those found in fields like finance, social sciences, or environmental studies. This would help to evaluate the generalizability and robustness of the method in various contexts. Additionally, developing a comprehensive framework for the automated assessment of feature-selection stability in multi-omics data is another promising direction. This framework could integrate multiple stability metrics, incorporate machine-learning algorithms tailored to different omics types, and provide visualization tools to aid in interpreting stability outcomes. Such advancements would enhance the usability and effectiveness of stability assessments in complex, high-dimensional datasets, ultimately contributing to more reliable and reproducible feature-selection processes across diverse scientific disciplines.  
In concluding the discussion, it is essential to acknowledge the limitations of the presented research. We take into consideration only four omic layers, which may not capture the full complexity of biological systems. In reality, there are additional omic layers, such as methylation, which can provide valuable insights. By excluding these layers, the analysis may overlook critical interactions and regulatory mechanisms that could influence the results. Future studies should aim to include a broader range of omic layers to provide a more holistic understanding and to better reflect the intricacies of multi-omics datasets.