Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links

Abhik Seal,David J Wild

doi:10.1186/s12859-018-2254-7

Abstract

BackgroundNetpredictor is an R package for prediction of missing links in any given unipartite or bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm and a combination of both. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing. The application can also be used to search for top-K shortest paths between interactome and use enrichment analysis for disease, pathway and ontology. The R standalone package (including detailed introductory vignettes) and associated R Shiny web application is available under the GPL-2 Open Source license and is freely available to download.ResultsWe compared different algorithms performance in different small datasets and found random walk supersedes rest of the algorithms. The package is developed to perform network based prediction of unipartite and bipartite networks and use the results to understand the functionality of proteins in an interactome using enrichment analysis.ConclusionThe rapid application development envrionment like shiny, helps non programmers to develop fast rich visualization apps and we beleieve it would continue to grow in future with further enhancements. We plan to update our algorithms in the package in near future and help scientist to analyse data in a much streamlined fashion.

Highlights

Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network
One way to formulate the problem of DTI prediction as a binary classification problem, where the drug-target pairs are treated as instances, and the chemical structures of drugs and the amino acid subsequences of targets are treated as features
The R package described in this paper provides utilities to compute recommendations in a bipartite network and well as unipartite network based on HeatS [15], Random walk with Restart (RWR) [23, 24], Network based inference (NBI) [16, 21, 22] and combination of RWR and NBI(netcombo)

Summary

Introduction

Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm and a combination of both. Traditional machine learning algorithms like Naive Bayes, SVM and Random Forest have been successfully applied to predict drug target relations [1,2,3,4]. Liu et al [33] have developed PyDTI package which mainly focuses on neighborhood regularized logistic matrix factorization (NRLMF). NRLMF uses logistic matrix factorization and neighbouhood regularization to prediction drug target pairs. The PyDTI package provides access to other algorithms for drug target prediction such as

Methods

Results

Conclusion