Identification of Pathway-Specific Protein Domain by Incorporating Hyperparameter Optimization Based on 2D Convolutional Neural Network

Pathway-specific protein domain (PSPD) are associated with specific pathways. Many protein domains are pervasive in various biological processes, whereas other domains are linked to specific pathways. Many human disease pathways, such as cancer pathways and signaling pathway-related diseases, have caused the loss of functional PSPD. Therefore, the creation of an accurate method to predict its roles is a critical step toward human disease and pathways. In this study, we proposed a deep learning model based on a two-dimensional neural network (2D-CNN-PSPD) with a pathway-specific protein domain association prediction. In terms of the purposes of a sub-pathway, its parent pathway and its super pathway are linked to the Uni-Pathway. We also proposed a dipeptide composition (DPC) model and a dipeptide deviation (DDE) model of feature extraction profiles as PSSM. Then, we predicted the proteins associated with the same sub-pathway or with the same organism. The DDE model and DPC model of the PSSM feature profile input was associated with our proposed 2D-CNN method. We deployed several parameters to optimize the model's output performance and used the hyperparameter optimization approach to find the best model for our dataset based on the 10-fold cross-validation results. Ultimately, we assessed the predictive performance of the current model by using independent datasets and cross-validation datasets. Therefore, we enhanced the efficiency of deep learning methods. PSPD is involved in any known pathway and then follow the association in different stages of the pathway hierarchy with other proteins. Our proposed method could identify 2D-CNN-PSPD with 0.83% sensitivity, 0.92% specificity, 87.27% accuracy, and 0.75% accuracy. We provided an important method for the analysis of PSPD proteins in the proposed research, and our achievements might promote computational biological research. We concluded our proposed model architecture in the future, the use of the latest features, and the multi-one structure to predict different types of molecules, such as DNA, RNA, and disease-pathway specific proteins associations.