Incorporating Distance-Based Top-n-gram and Random Forest To Identify Electron Transport Proteins

Cellular respiration provides direct energy substances for living organisms. Electron storage and transportation should be completed through electron transport chains during the cellular respiration process. Thus, identifying electron transport proteins is an important research task. In protein identification, selection of the feature extraction method and classification algorithm has a direct bearing on classification. The distance-based Top-n-gram method, which was proposed based on the frequency profile and considered evolutionary information, was used in this study for feature extraction. The Max-Relevance-Max-Distance algorithm was adopted for feature selection. The first 4D features that greatly influenced the classification result were selected to form the feature data set. Finally, the random forest algorithm was used to identify electron transport proteins. Under the 10-fold cross-validation of the model constructed in this study, sensitivity, specificity, and accuracy rates surpassed 85%, 80%, and 82%, respectively. In the testing set, F-measure, AUC value, and accuracy exceeded 74%, 95%, and 86%, respectively. These experimental results indicated that the classification model built in this study is an effective tool in identifying electron transport proteins.