  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.
