DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation

Abstract

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

Semi-supervised spectral clustering using shared nearest neighbour for data with different shape and density

“…SNN is based on the fact that if two points are similar to some of the same points, they are similar even if a direct similarity measure cannot indicate them [25]. More specifically, as long as two objects are in each other's nearest neighbor's table, SNN similarity is the number of nearest neighbors they share [26]. Suppose that two objects A and B have 8 closest neighbors, and the two objects contain each other.…”

Drug–Target Interaction Prediction Model Using Optimal Recurrent Neural Network

“…Islam et al [16] presented DTI-SNNFRA, an architecture to predict DTI, on the basis of fuzzy-rough approximation (FRA) and shared nearest neighbour (SNN). It applies sampling models to jointly decrease the searching space covering the available targets, drugs, and millions of interactions amongst others.…”

An ensemble-based drug–target interaction prediction approach using multiple feature information with data balancing

“…Table 1 shows that DTI-SNFRA [30] works in two phases: first, it uses an SNN, followed by a search spacepartitioning group, and then, it calculates the degree of fuzzy-raw approximation and selects the appropriate degree threshold for excitation samples' undercounting from all possible drug-target interaction pairs obtained in the first stage. In [31] and [16] the deep learning structures models discovered local survival patterns the target successfully enriches protein advantages of the raw protein sequence, leading to greater predictive results than related approaches.…”