The innovations in functional genomics have provided a pathway for the identification and prediction of potential druggable human genes that help in the innovation of drug discovery and development. This is obtained through hybrid optimization techniques that involve decision trees and random forest algorithms. This helps to identify the genome-wide druggable human genes using functional genomics data. This is achieved through multiple stages of its analysis. The first stage involves the collection of genomic and proteomic data with numerous disease classifications and tissue structures. The data quality and normalization are achieved through data preprocessing techniques through the integration of various parameters. The hybrid optimization process functions with the aid of a decision tree. These are the primary classifiers that help to determine the individual features within the datasets. This helps to obtain the fundamental selection of potential druggable gene candidates. This helps to provide both the numerical and categorical data. This is suitable for the multifaceted nature of functional genomics data structures. Then the random forest algorithm connects the strength of multiple decision trees to improve the predictive accuracy and overfitting process. Feature importance score is obtained from the random forest model that provides the functional information of the genes with disease mechanisms. The predictive capabilities of the proposed approach are achieved through a cross-validation process. Comparative analysis is done with the proposed system with the existing model through analyzing various performance matrices involving AUC-ROC curves. This helps to obtain the complex relationships between genomic features and druggability. The proposed model provides various innovations in the drug discovery process.
 

Druggable human genes, Deep learning, Hybrid optimization techniques, data quality, Normalization, Drug discovery process, Performance matrices