A Framework for Identifying Essential Proteins with Hybridizing Deep Neural Network and Ordinary Least Squares

Essential proteins are vital for maintaining life activities and play a crucial role in biological processes. Identifying essential proteins is of utmost importance as it helps in understanding the minimal requirements for cell life, discovering pathogenic genes and drug targets, diagnosing diseases, and comprehending the mechanism of biological evolution. The latest research suggests that integrating protein–protein interaction (PPI) networks and relevant biological sequence features can enhance the accuracy and robustness of essential protein identification. In this paper, a deep neural network (DNN) method was used to identify a yeast essential protein, which was named IYEPDNN. The method combines gene expression profiles, PPI networks, and orthology as input features to improve the accuracy of DNN while reducing computational complexity. To enhance the robustness of the yeast dataset, the common least squares method is used to supplement absenting data. The correctness and effectiveness of the IYEPDNN method are verified using the DIP and GAVIN databases. Our experimental results demonstrate that IYEPDNN achieves an accuracy of 84%, and it outperforms state-of-the-art methods (WDC, PeC, OGN, ETBUPPI, RWAMVL, etc.) in terms of the number of essential proteins identified. The findings of this study demonstrate that the correlation between features plays a crucial role in enhancing the accuracy of essential protein prediction. Additionally, selecting the appropriate training data can effectively address the issue of imbalanced training data in essential protein identification.