To extend the EODE framework to incorporate other types of omics data such as proteomics and metabolomics for a more comprehensive cancer profiling, several modifications and enhancements can be implemented:
Feature Engineering: The feature selection process in EODE can be adapted to handle the unique characteristics of proteomics and metabolomics data. Specific feature selection algorithms tailored for these data types can be integrated into the framework to identify relevant biomarkers.
Data Integration: Incorporating multiple omics data types requires robust data integration techniques. Methods such as multi-omics data fusion and integration algorithms can be employed to combine different types of omics data effectively.
Model Adaptation: The ensemble learning models in EODE can be modified to accommodate the diverse nature of proteomics and metabolomics data. Customized ensemble strategies that consider the specific characteristics of each omics data type can be developed.
Validation and Evaluation: Rigorous validation and evaluation procedures need to be established to assess the performance of the extended EODE framework on integrated omics data. Cross-validation techniques and independent validation datasets can be utilized for comprehensive assessment.
By incorporating these enhancements, the extended EODE framework can provide a holistic approach to cancer profiling by leveraging multiple omics data types for more accurate and comprehensive analysis.