An integrative approach to develop computational pipeline for drug-target interaction network analysis

Bansal, Ankush; Srivastava, Pulkit Anupam; Singh, Tiratha Raj

doi:10.1038/s41598-018-28577-6

Cited by 8 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several methods developed based on these assumptions are summarized in [23], and most of these methods achieved promising results. Network-based methods formulate the prediction of DTIs as link prediction problem in a heterogeneous graph [19][20][21][34][35][36][37][38]. For example, DASPfind [19] constructs a DTIs graph using a drug-drug similarity matrix, target-target similarity matrix, and known DTIs.…”

Section: Introductionmentioning

confidence: 99%

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

et al. 2020

View full text Add to dashboard Cite

In silico prediction of drug-target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drugtarget interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug-Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug-target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug-target interactions graph with two other complementary graphs namely: drug-drug similarity, target-target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug-drug similarities and target-target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.

show abstract