DOI: 10.57760/sciencedb.16326
title: Key protein recognition model
title: Key protein recognition model

description: This model is a computational framework for identifying essential proteins in biological networks, aimed at improving prediction accuracy and efficiency through various advanced machine learning techniques. The program first uses the node2vec algorithm to embed proteins in the protein-protein interaction (PPI) network as low dimensional vectors, capturing structural information such as neighborhood relationships and topological features in the network, thereby effectively expressing the patterns of protein interactions. Next, the program extracts various biological features from the amino acid sequence of the protein, including amino acid composition, dipeptide frequency, and physicochemical properties. At the same time, deep learning is used to analyze the position specific scoring matrix (PSSM), extract evolutionary information, and further improve the accuracy of prediction. After feature extraction is completed, the program inputs these features into multiple machine learning classifiers, such as Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Tree (GBDT), to classify and predict proteins. In order to further improve the robustness and accuracy of the model, the program adopts ensemble learning methods (such as voting, weighted average, and stacking) to fuse the prediction results of different classifiers and enhance the overall performance of the model. Through the combination of multiple levels and technologies, the program can accurately identify essential proteins and provide efficient computational support for related biological research. This framework has demonstrated good performance and reliability in processing large-scale biological data. 
description: This model is a computational framework for identifying essential proteins in biological networks, aimed at improving prediction accuracy and efficiency through various advanced machine learning techniques. The program first uses the node2vec algorithm to embed proteins in the protein-protein interaction (PPI) network as low dimensional vectors, capturing structural information such as neighborhood relationships and topological features in the network, thereby effectively expressing the patterns of protein interactions. Next, the program extracts various biological features from the amino acid sequence of the protein, including amino acid composition, dipeptide frequency, and physicochemical properties. At the same time, deep learning is used to analyze the position specific scoring matrix (PSSM), extract evolutionary information, and further improve the accuracy of prediction. After feature extraction is completed, the program inputs these features into multiple machine learning classifiers, such as Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Tree (GBDT), to classify and predict proteins. In order to further improve the robustness and accuracy of the model, the program adopts ensemble learning methods (such as voting, weighted average, and stacking) to fuse the prediction results of different classifiers and enhance the overall performance of the model. Through the combination of multiple levels and technologies, the program can accurately identify essential proteins and provide efficient computational support for related biological research. This framework has demonstrated good performance and reliability in processing large-scale biological data.