- Open Access
Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links
BMC Bioinformatics volume 19, Article number: 265 (2018)
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. 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.
We 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.
The 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.
Identifying missing associations between drugs and targets provides insights into polypharmacology and off-target mediated effects of chemical compounds in biological systems. Traditional machine learning algorithms like Naive Bayes, SVM and Random Forest have been successfully applied to predict drug target relations [1–4]. However, using supervised machine learning methods requires training sets, and they can suffer from accuracy problems through insufficient sampling or scope of training sets. During the last years, the field of semi-supervised learning has been applied to methods based on graphs or networks. The data points are represented as vertices of a network, while the links between the vertices depend upon the labeled information. Thus, it is desirable to develop a predictive model based on using both labeled and unlabeled information. Recently several machine learning techniques provides effective and efficient ways to predict DTIs. 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. Then, classical classification methods can be used, e.g., support vector machines (SVM) and regularized least square (RLS). Liu et al.  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 NetLapRLS,BLM-NII,KBMF-2k,CMF implemented in a single package. Bajic  have developed DDR package which combines multiple different similarity measures in the drug space and protein target space and optimizes using average entropy measures. Peska  developed bayesian ranking approach for drug target prediction. The novelty of the approach comes from “per-drug ranking” optimization criteria, while projecting drugs and targets to a shared latent space. Most of these methods are command line based and they need to have prior programming expertise to start the analysis. Netpredictor solves this problem by building an intuitive UI and giving users an easy way to interaction and peform prediction based on their data. The main advantages of network-based methods are:
They use label information and as well as unlabeled data as input in the form of vectors.
Once can use multiple classes inside the network structure.
It uses multitude of paths to compute associations.
Network based methods mostly use transductive learning strategy,in which the test set is unlabelled but while computation it uses the information from neighbourhood.
The ranked list of proteins can be also be used to understand any protein proteins interactions exist among them using subgraph extraction or it can be used to understand neighbouring PPIs in the interactome. Such kind of networks helps in understanding of pathogenic and physiologic mechanisms that trigger the onset and progression of diseases. To dig deeper into such cases, the list of proteins can be used to perform Gene Ontology,disease and pathway enrichment to understand the mechanism of action of proteins and whether if that target is a suitable target or not.
In the PPI Network tab consist of three functionalities namely one can search for protein interaction from a list of proteins, search for top-k PPI shortest paths using Yen’s algorithm  using both weighted and un-weighted graphs. The algorithm executes O(n) times Dijkstra algorithm to search paths for each of the k shortest paths, so its time complexity is O(kn(m+nlogn)), where n is the number of nodes and m is the number of edges. Shortest path graph algorithm has been widely adopted to identify genes with important functions in a network [26–30].
Main features of netpredictor standalone and web tool
The standalone R package application can perform prediction on unipartite networks using a set of different similarity measures between vertices of a graph in order to predict unknown edges (links) [34–36]. The prediction methods are classified into two categories:
Neighborhood based metrics and
Path based metrics
For neighbourhood based metrics the methods which are implemented are (i) common neighbours (ii) jaccard coefficient  (iii) cosine similarity (iv) hub promoted index  (v) hub depressed index (vi) Adamic Adar index  (vii) Preferential attachment  (viii) Resource allocation  (ix) Leicht-Holme-Nerman Index . Similarly using path-based metrics one can compute paths between two nodes as similarity between node pairs. The methods are:
The local path based metric  uses the path of length 2 and length 3. The metric uses the information of the nearest neighbours and it also uses the information from the nodes within length of 3 distances from the current node.
The Katz metric  is based on similarity of all the paths in a graph.This method counts all the paths between given pair of nodes with shorter paths counting more heavily. Parameters are exponential.
Geodesic similarity metric calculates similarity score for vertices based on the shortest paths between two given vertices.
Hitting time  is calculated based on a random walk starts at a node x and iteratively moves to a neighbor of x chosen uniformly at random. The Hitting time Hx,y from x to y is the expected number of steps required for a random walk starting at x to reach y.
Random walk with restart [16, 45, 46] is based on pagerank algorithm . To compute proximity score between two vertexes we start a random walker at each time step with the probability 1 - c, the walker walks to one of the neighbors and with probability c, the walker goes back to start node. After many time steps the probability of finding the random walker at a node converges to the steady-state probability.
The significance of interaction of links is based on random permutation testing. A random permutation test compares the value of the test statistic predicted data value to the distribution of test statistics when the data are permuted. Supporting Information S1_NetpredictorVignette provides tutorial for this netpredictor standalone R package. In the web application app one can load their own data or can use the given sample datasets used in the software. For the custom dataset option one needs to upload bipartite adjacency matrix along with the drug similarity matrix and protein sequence matrix. From the given datasets Enzyme, GPCR, Ion Channel and Nuclear Receptor in the application one can load the data and set the parameters for the given algorithms and start computations. The data structure the web application accepts matrix format files for computation.
A summary of the contents of each of the tabs shiny netpredictor application is reported in Table 2.
Start prediction tab
The start prediction tab is designed to upload a network in matrix format and compute it properties, searching for modules, fast prediction of missing interactions, visualization of bipartite modules and predicted network. For the custom dataset, in the input drug-target binary matrix, target nodes should be in rows and drug nodes in the columns. The drug similarity matrix and the target similarity should have the exact number of drugs and targets from the binary matrix. For HeatS, only the bipartite network is used to compute the recommendation of links. For RWR, NBI, and Netcombo all of these require three matrices. The default parameters are already being set for the algorithms. The main panel of the start prediction tab has four tabs that compute network properties, network modules, the prediction results and predicted network plot.
One can also perform advance analysis using two tabs namely - statistical analysis tab and permutation testing. The statistical analysis tab computes the performance of the algorithms. Three algorithms are network based inference, random walk with restart and netcombo can be used. One can randomly remove the true links from the network using frequency of the drug target interactions in the network. The performance of the algorithm is checked when the removed links are repredicted. The statistics used to evaluate the performance is AUAC, AUC, AUCTOP(10%), Boltzmann-enhanced discrimination of ROC (BEDROC)  and enrichment factor(EF). The data table gets automatically updated for each of the computations. The results are reported in main panel using data tables. The significance of interactions using random permutations can be computed for the given network using network based inference and random walk with restart. The networks are randomized and significance of the interactions are calculated based on standard normal distribution. The user needs to give total number of permutations to compute and the significant interactions to keep.
In the current application we used human protein-protein interaction (PPI) data from both consensuspathDB(CPDB) and string DB. The data sources are converted to igraph objects for faster loading and computation. We have implemented top-K shortest paths search using Yen’s algorithm (), with PPI in both the datasets. The multiple shortest path proteins can be enriched for reactome pathways using over-representation analysis. We also provide sub-graph extraction from the PPI datasets using a large list of proteins. can be useful for connecting sources to targets in protein networks, a problem that has been the focus of many studies in the past which include discovering genomic mutations that are responsible for changes in downstream gene expression  studying interactions between different cellular processes  and linking environmental stresses through receptors to transcriptional changes. The details are of the PPI tab are discussed in the supplemental information.
The drugbank tab helps to search predicted interactions computed using NBI method using the drugbank database. One can search for targets given a specific drugbank ID and search for drugs given a specific hugo gene name. The Enrichment Analysis tab helps to search the relevant gene ontology terms,pathways and diseases for a given list of genes. A search can be made based on predicted proteins and in order to understand its function, location and pathway this tab can help to understand it. The level of ontology can also be given to the user input. We used biomart services using the biomaRT R package to convert genes names to entrez ids and then the clusterProfiler R package () to retrieve the gene ontology lists. The pathway enrichment is based on the ReactomePA R package ().
Search drugbank tab
The drugbank tab Fig. 4 helps to search predicted interactions computed using NBI method using the drugbank database . One can search for targets given a specific drugbank ID and search for drugs given a specific hugo gene name . In Fig. 3 the data table shows the drug target significant scores whether it is a true or predicted interaction, Mesh categories of drugs, ATC codes and groups (approved, illicit,withdrawn, investigational, experimental). Currently the drugbank search tab only supports data computed using Network based inference. The computed results and the associated meta-data are stored in a sqllite database  for access through shiny data tables interface.
Ontology and pathway search tab
The Ontology and pathway search tab Fig. 5 helps to search the relevant gene ontology terms and pathways for a given set of genes. A search can be made based on predicted proteins and in order to understand its function, location and pathway this tab can help to understand it. The level of ontology can also be given to the user input. We used biomart services using the biomaRT R package  to convert genes names to entrez ids and then the clusterProfiler R package  to retrieve the gene ontology lists. The pathway enrichment is based on the ReactomePA R package .
Results and discussion
In this section we illustrate the use of Netpredictor package in prediction of drug target interactions and analysis of networks. The information about the interactions between drugs and target proteins was obtained from Yamanishi et al.  where the number of drugs 212, 99, 105 and 27, interacting with enzymes, ion channels, GPCRs and nuclear receptors respectively. The numbers of the corresponding target proteins in these classes are 478, 146, 84 and 22 respectively. The numbers of the corresponding interactions are 1515, 776, 314 and 44. We performed both network based inference and Random walk with restart on all of these datasets. To check the performance we randomly removed 20% of the interactions from each of the dataset and computed the performance 50 times and calculated the mean performance of each of these methods. The results are given in Table 3. Clearly, RWR supersedes its performance compared to network based inference in Enzyme and the GPCR dataset. However, computation of NBI algorithm takes less amount of time than RWR. For the drugbank tab we download the latest drugbank set version 4.3 and created a drug target interaction list of 5970 drugs and 3797 proteins We computed similarities of drugs using RDkit  ECFP6 fingerprint and local sequence similarity of proteins using smith waterman algorithm and normalized using the procedure proposed by Bleakley and Yamanishi  and integrated the matrices for network based inference computation. We ran the computations 50 times and kept the significant drug target relations (p ≤ 0.05) where a total of 316645 predicted interactions and 14167 true interactions present in the system.
In this paper we presented netpredictor, a standalone and web application for drug target interaction prediction. Netpredictor uses a shiny framework to develop web pages and the application can be accessed from web browsers. To set up the Netpredictor application locally there are some additional requirements other than shiny which are given below,
Firstly, the user has to have the R statistical environment installed, for which instructions can be found in R software home page.
Secondly, the devtools R package  has to be installed. The package can be installed using devtools R package.
Also for fast computation Microsoft R Open package needs to be installed which can be obtained from https://mran.revolutionanalytics.com/documents/rro/installation/. Microsoft R Open includes multi-threaded math libraries to improve the performance of R. R is usually single threaded but if its linked to the multi-threaded BLAS/LAPACK libraries it can perform in multi-threaded manner. This usually helps in matrix multiplications, decompositions and higher level matrix operations to run in parallel and minimize computation times.
After installing R, R open and shiny calling shiny::runGitHub(’Shiny_NetPredictor’, ’abhik1368’)
This will load all the libraries need to run netpredictor in browser. The application can be accessed in any of the default web browsers. The netpredictor R package (https://github.com/abhik1368/netpredictor) and the Shiny Web application(https://github.com/abhik1368/Shiny_NetPredictor) is freely available. Users can follow the “Issues” link on the GitHub site to report bugs or suggest enhancements. In future the intention is to include Open Biomedical Ontologies for proteins to perform enrichment analysis. The package is scalable for further development integrating more algorithms.
Availability and requirements
Project name: shiny_Netpredictor
Project home page:https://github.com/abhik1368/ShinyNetPredictor)
Operating system(s): Platform independent
Programming language: R
Other requirements: R environment including digest and tools packages. Tested on R version 3.4
License: GNU GPL
Any restrictions to use by non-academics: no restrictions
Anatomical therapeutic chemical
Extendend connectivity fingerprints
Network based inference
Protein - protein interactions
Random walk with restart
Cao DS, Liang YZ, Yan J, Tan GS, Xu QS, Liu S. PyDPI: Freely Available Python Package for Chemoinformatics, Bioinformatics, and Chemogenomics Studies. J Chem Inf Model. 2013; 53(11):3086–3096. https://doi.org/10.1021/ci400127q.
Cao DS, Liang YZ, Deng Z, Hu QN, He M, Xu QS, Zhou GH, Zhang LX, Zx Deng, Liu S. Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach. PloS one. 2013a; 8(4):e57680.
van Westen GJP, Wegner JK, IJzerman AP, van Vlijmen HWT, Bender A. Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets. Med Chem Comm. 2010; 2:16–30.
Paricharak S, Cortés-Ciriano I, IJzerman AP, Malliavin TE, Bender A. Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules. J Cheminformatics. 2015; 7:15.
Luna A, Rajapakse VN, Sousa FG, Gao J, Schultz N, Varma S, Reinhold W, Sander C, Pommier Y. rcellminer: exploring molecular profiles and drug response of the NCI-60 cell lines in R. Bioinformatics. 2016; 32(8):1272–1274.
Ghazanfar S, Yang JY. Characterizing mutation-expression network relationships in multiple cancers. Comput Biol Chem. 2016; 63:73–82.
Lakshmanan K, Peter AP, Mohandass S, Varadharaj S, Lakshmanan U, Dharmar P. SynRio: R and Shiny based application platform for cyanobacterial genome analysis. Bioinformation. 2015; 11(9):422–5.
Klambauer G, Wischenbart M, Mahr M, Unterthiner T, Mayr A. Hochreiter S.Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map. Bioinformatics. 2015; 31(20):3392–4.
Walter W, Sánchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis.Bioinformatics. 2015; 31(17):2912–4.
Hinterberg MA, Kao DP, Bristow MR, Hunter LE, Port JD. Görg C.Peax: interactive visual analysis and exploration of complex clinical phenotype and gene expression association. Pac Symp Biocomput. 2015:419–30. https://doi.org/10.1142/9789814644730_0040.
Mallona I, Díez-Villanueva A, Peinado MA. Methylation plotter: a web tool for dynamic visualization of DNA methylation data. Source Code Biol Med. 2014; 9:11. https://doi.org/10.1186/1751-0473-9-11. eCollection 2014.
Peska L, Buza K, Koller J. Drug-target interaction prediction: A Bayesian ranking approach Comput. Methods Programs Biomed. 2017; 152:15–21.
R Core Team. R: A Language and Environment for Statistical Computing. 2013. Available from: http://www.r-project.org/.
Chang W, Cheng J, Allaire J, Xie Y, McPherson J. shiny: Web Application Framework for R. 2015. R package version 0.11.1. Available from: http://CRAN.R-project.org/package=shiny.
Zhou T, et al.Solving the apparent diversity-accuracy dilemma of recommender systems. Proc Natl Acad Sci USA. 2010; 107:4511–5.
Zhou T, et al.Bipartite network projection and personal recommendation. Phys Rev E Stat Nonlin Soft Matter Phys. 2007; 76:046115.
Olayan RS, Ashoor H, Bajic VB. DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches. Bioinformatics. 2018; 34(7):1164–73. https://doi.org/10.1093/bioinformatics/btx731.
Liu X, Murata T. Community Detection in Large-Scale Bipartite Networks. IEEE Comput Soc. 2009; 1:50–57.
Yen JY. Finding the K Shortest Loopless Paths in a Network. Mangement Sci. 1971; 17(11):712–716.
Poisot T. lpbrim: Optimization of bipartite modularity using LP-BRIM (Label propagation followed by Bipartite Recursively Induced Modularity). R package version 1.0.0 2015.
Cheng F, et al.Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol. 2012; 8:e1002503.
Alaimo S, Pulvirenti A, Giugno R, Ferro A. Drug-target interaction prediction through domain-tuned network-based inference. Bioinformatics. 2013; 29(16):2004–8.
Chen X, et al.Drug–target interaction prediction by random walk on the heterogeneous network. Mol BioSyst. 2012; 8:1970–8.
Seal A, Ahn Y, Wild DJ. Optimizing drug target interaction prediction based on random walk on heterogeneous networks. J Cheminformatics. 2015; 7:40.
Chen L, Huang T, Zhang YH, Jiang Y, Zheng M, Cai YD. Identification of novel candidate drivers connecting different dysfunctional levels for lung adenocarcinoma using protein–protein interactions and a shortest path approach. Sci Rep. 2016; 6:29849.
Jiang M, Chen Y, Zhang Y, Chen L, Zhang N, Huang T, Cai YD, Kong XY. Identification of hepatocellular carcinoma related genes with k-th shortest paths in a protein—Protein interaction network. Mol BioSyst. 2013; 9:2720–8.
Chen L, Xing Z, Huang T, Shu Y, Huang G, Li HP. Application of the shortest path algorithm for the discovery of breast cancer related genes. Curr Bioinform. 2016; 11:51–8.
Li BQ, Huang T, Liu L, Cai YD, Chou KC. Identification of colorectal cancer related genes with mRMR and shortest path in protein–protein interaction network. PLoS ONE. 2012; 7:e33393.
Chen L, Yang J, Huang T, Kong XY, Lu L, Cai YD. Mining for novel tumor suppressor genes using a shortest path approach. J Biomol Struct Dyn. 2016; 34:664–75.
Kamburov A, Stelzl U, Lehrach H, Herwig R. The ConsensusPathDB interaction database: 2013 update. Nucleic Acids Res. 2013; 41(D1):D793—800.
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, von Mering C. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017; 45:D362–68.
Liu Y, Wu M, Miao C, Zhao P, Li X-L. Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction. PLoS Comput Biol. 2016; 12(2):e1004760.
Liben-Nowell D, Kleinberg JM. The link prediction problem for social networks. J Comput Aided Mol Des. 2003; CIKM:556–9.
Hasan MA, Zaki MJ. A survey of link prediction in social networks. Soc Netw Data Analytics. 2011:243–75.
Liben-Nowell D, Kleinberg JM. The link prediction problem for social networks. J Comput Aided Mol Des. 2003; CIKM:556–9.
Jaccard P. Etude comparative de la distribution florale dans une por-tion des alpes et de jura. Bull Soc Vaudoise Sci Nat. 1901; 37:547–79.
Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabasi A-L. Hierarchical organization of modularity in metabolic networks. Science. 2002; 297:1553.
Adamic LA, Adar E. Friends and neighbors on the web. Soc Networks. 2002; 25(3):211–30.
Barabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999; 286:509–12.
Zhou T, Lu L, Zhang YC. Predicting missing links via local information. Eur Phys JB. 2010; 71:623–30.
Leicht EA, Holme P, Newman MEJ. Vertex similarity in networks. Phys RevE. 2006; 73:026120.
Lu L, Jin CH, Zhou T. Similarity index based on local paths for link prediction of complex networks. Phys Rev E. 2009; 046122:80.
Katz L. A new status index derived from sociometric analysis. Psychometrika. 1953; 18:39–43.
Fouss F, Pirotte A, Renders J-M, Saerens M. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans Knowl Data Eng. 2007; 19:355–69.
Kohler S, Bauer S, Horn D, Robinson1 PN. Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet; 82:949–958.
Langville AN, Meyer CD. Google’s pagerank and beyond: the science of search engine rankings: Princeton University Press; 2012.
rCharts. [cited 4.1.2016]. Available from: https://ramnathv.github.io/rCharts/.
Barber M. Modularity and community detection in bipartite networks. Phys Rev E. 2007; 76:066102.
Suthram S, Beyer A, Karp RM, et al.eQED: an efficient method for interpreting eQTL associations using protein networks. Mol Syst Biol. 2008; 4:162. 10.1038/msb.2008.4.
Yosef N, Zalckvar E, Rubinstein AD, et al.ANAT: a tool for constructing and analyzing functional protein networks. Sci Signal. 2011; 4(196). pl1. 10.1126/scisignal.2001935.
DataTables. [cited 4.1.2016]. Available from: https://www.datatables.net/.
visNetwork. [cited 4.1.2016]. Available from: http://dataknowledge.github.io/visNetwork/.
Htmlwidgets. [cited 4.1.2016]. Available from: http://www.htmlwidgets.org/.
Truchon J-F, Bayly CI. Evaluating VS methods: good and bad metrics for the early recognition problem. J Chem Inf Model. 2007; 47:488–508.
Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2011; 39(Database issue):D514–9. Epub 2010 Oct 6.
Gray KA, Yates B, Seal RL, Wright MW, Bruford EA. Genenames.org: the HGNC resources in 2015. Nucleic Acids Res. 2015; 43(Database issue):D1079–85. https://doi.org/10.1093/nar/gku1071. Epub 2014 Oct 31.
Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics. 2005; 21:3439–40.
Yu G, Wang L, Han Y, He Q. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS J Integr Biol. 2012; 16(5):284–7.
Yu G, He Q. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol BioSyst. 2016; 12:477–9.
Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics. 2008; 24:i232–40.
Devtools by HadleyWickham. https://github.com/hadley/devtools.
RDKit. Cheminformatics and Machine Learning Software. 2013. http://www.rdkit.org.
Bleakley K, Yamanishi Y. Supervised prediction of drug–target interactions using bipartite local models. Bioinformatics. 2009; 25:2397–403.
Authors wish to thank anonymous reviewers for their critiques and constructive comments which significantly improved this manuscript. Authors also wish to acknowledge these individuals for their comments on this project: Dr. Yong Yeol Ahn and Dr. Ying Ding. Authors would also like to thank BMC editors who have waived 50% of the article processing fee.
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Seal, A., Wild, D.J. Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links. BMC Bioinformatics 19, 265 (2018). https://doi.org/10.1186/s12859-018-2254-7
- Enrichment analysis
- R shiny