Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network

Abstract

Molecular components that are functionally interdependent in human cells constitute molecular association networks. Disease can be caused by disturbance of multiple molecular interactions. New biomolecular regulatory mechanisms can be revealed by discovering new biomolecular interactions. To this end, a heterogeneous molecular association network is formed by systematically integrating comprehensive associations between miRNAs, lncRNAs, circRNAs, mRNAs, proteins, drugs, microbes, and complex diseases. We propose a machine learning method for predicting intermolecular interactions, named MMI-Pred. More specifically, a network embedding model is developed to fully exploit the network behavior of biomolecules, and attribute features are also calculated. Then, these discriminative features are combined to train a random forest classifier to predict intermolecular interactions. MMI-Pred achieves an outstanding performance of 93.50% accuracy in hybrid associations prediction under 5-fold cross-validation. This work provides systematic landscape and machine learning method to model and infer complex associations between various biological components.

Keywords: Biocomputational Method; Bioinformatics; Computational Bioinformatics.

Graphical abstract

Figure 1

The Workflow of the MMI-Pred The molecular association network is formed by connecting multitype intermolecular associations among mRNAs, proteins, miRNAs, lncRNAs, circRNAs, drugs, microbes, and diseases. Both the handcrafted attribute features and behavior features learned by network embedding method of biomolecules are jointly fed into a random forest classifier for training to predict potential intermolecular interactions.

Figure 2

The Number and Type Distribution of Biomolecule Nodes and Intermolecular Associations in the Molecular Association Network

Figure 3

The Performance of MMI-Pred on Entire MAN Dataset under 5-Fold Cross-Validation On the left is the ROC curve and AUC value, and on the right is the precision-recall curve and AUPR value.

Figure 4

The Comparison of Network Behavior and Attribute Features Using Random Forest Classifier On the left is the ROC curve and AUC value, and on the right is the PR curve and AUPR value.

Figure 5

The Performance Comparison between MMI-Pred and Four Different Comparison Models Include Naive Bayes, Adaboost, Logistic Regression, and XGBoost Classifiers