Identification of Human Membrane Protein Types by Incorporating Network Embedding Methods

Abstract

Membrane protein is an important type of proteins and has been confirmed to play essential roles in various cellular processes. Based on their intramolecular arrangements and positions in a cell, they can be categorized into several types. However, it is time-and cost-consuming to recognize the type of a given membrane protein via traditional biophysical methods. In view of this, several computational models have been proposed in recent years. Most models adopted various information of membrane proteins, such as their sequences, domain profiles, physiochemical properties, etc. to extract different features, which were fed into downstream classification algorithms. In this study, we built two novel prediction models, which incorporated novel feature extraction methods, i.e., network embedding methods. To this end, several protein networks were constructed using the protein-protein interaction information retrieved from STRING. Among these models, one model was constructed based on features obtained by applying Mashup on seven protein networks, another model was built using features yielded by Node2Vec on one comprehensive protein network. Each model adopted random forest as the classification algorithm and employed the Synthetic Minority Over-sampling Technique (SMOTE) to overcome the influence yielded by the great difference on sizes of different membrane protein types. Furthermore, two models were integrated into one model to improve the predicted quality. The test results shown that the integrated model had good performance and was superior to any individual model. Also, we compared our models with some previous models, suggesting that our models were competitive.

Similarity-Based Machine Learning Model for Predicting the Metabolic Pathways of Compounds

“…The selection of classification algorithm is very important for constructing efficient classification models. In this study, a powerful and classic classification algorithm, RF [12], was adopted, which has been widely used to tackle several problems in bioinformatics [9,16,[20][21][22][23][24][25][26][27][28]. Its brief description was as below.…”

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

“…Classification Algorithm. Two robust machine learning techniques, i.e., SVM and RF, are applied to perform the prediction of DBPs, which have been widely used for many classification tasks in the field of computational biology [43][44][45][46]. SVM is an outstanding classification method that is used to deal with a binary pattern recognition problem [47].…”

Identifying Cell-Type Specific Genes and Expression Rules Based on Single-Cell Transcriptomic Atlas Data

“…RF (Breiman, 2001) is a supervised classifier comprising multiple decision trees, each of which is grown from a bootstrap set and a feature subset randomly selected from original features. RF has been widely used for many biological applications (Pan et al, 2010;Zhao et al, 2018;Zhao R. et al, 2019;Zhao X. et al, 2019;Zhang et al, 2019). One advantage of RF is that it does not require much effort in hyperparameter optimization; in general, only default parameters are necessary.…”