An Automated Model for Target Protein Prediction in PPI

Background: Essential proteins play a crucial role in most of the living organisms. The computer-based task of predicting essential proteins is important for target protein identification, disease treatment and suitable drug development. Objective: Traditionally, many experimental and centrality measures have been proposed by researchers to predict protein essentiality. Methods: The prediction accuracy, sensitivity, and specificity identified by traditional methods is very low. Results and Discussion: In this research work, a novel computational based approach such as NCKNN model has been proposed to identify the essential proteins. The proposed work uses a combination of network topology measure and machine learning model to predict the essential proteins. Conclusion: The proposed work shows a remarkable improvement than seven traditional centrality based measures such as DC, BC, CC, EC, NC, ECC and SC in terms of the metrics such as accuracy (A1), precision (P1), recall (R1), sensitivity (SE) and specificity (SP).