Higher order organizational features can distinguish protein interaction networks of disease classes: a case study of neoplasms and neurological diseases

TL;DR

This study investigates whether local wiring patterns in protein interaction networks can distinguish disease classes like neoplasms and neurological diseases, using machine learning classifiers on orbit usage profiles.

Contribution

It introduces non-redundant orbit usage profiles as novel network features for classifying disease-related protein interaction networks, demonstrating superior performance of deep neural networks.

Findings

DNN achieved an average AUPRC of 0.988

nrOUPs-based classifier outperformed node2vec embeddings

Deep neural networks showed the best overall performance

Abstract

Neoplasms (NPs) and neurological diseases and disorders (NDDs) are amongst the major classes of diseases underlying deaths of a disproportionate number of people worldwide. To determine if there exist some distinctive features in the local wiring patterns of protein interactions emerging at the onset of a disease belonging to either of these two classes, we examined 112 and 175 protein interaction networks belonging to NPs and NDDs, respectively. Orbit usage profiles (OUPs) for each of these networks were enumerated by investigating the networks' local topology. 56 non-redundant OUPs (nrOUPs) were derived and used as network features for classification between these two disease classes. Four machine learning classifiers, namely, k-nearest neighbour (KNN), support vector machine (SVM), deep neural network (DNN), random forest (RF) were trained on these data. DNN obtained the greatest average AUPRC (0.988) among these classifiers. DNNs developed on node2vec and the proposed nrOUPs embeddings were compared using 5-fold cross validation on the basis of average values of the six of performance measures, viz., AUPRC, Accuracy, Sensitivity, Specificity, Precision and MCC. It was found that nrOUPs based classifier performed better in all of these six performance measures.