Optimized Ensemble Learning Framework for Prioritizing Biomolecular Markers in Yield Prediction Efficiency Enhancement

Biomolecular markers play a pivotal role in detecting various outputs present in living organisms. Each of these marker’s aids in gaging the existence of specific proteins, thereby enhancing output detection. Currently, these markers are employed to detect diverse outputs, such as those related to different cancer and disease types. The algorithms developed to discern these outputs operate by ranking these biomolecular markers based on their genomic significance. A review of existing research reveals that present models for marker ranking target a single output type, constraining their adaptability and efficacy for real-world clinical use. Additionally, these models suffer from extended training and validation periods, impeding their practical utility. To overcome these limitations, our research introduces an advanced ensemble learning approach to amplify output detection proficiency. We applied our approach to various outputs, including different disease types. Our findings underscore the model’s superior accuracy and speed compared to current top-tier methods. This enhancement is primarily due to the integration of GoogLeNet and Inception Net architectures, optimizing feature differentiation, thus heightening ranking efficiency levels. The proposed model has better precision, higher accuracy, and better recall than existing methods.