Main Features of netpredictor package and shiny 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) \cite{bib13,bib14}. The prediction methods are classified into two categories:
For neighbourhood based metrics the methods which are implemented are (i) common neighbours (ii) jaccard coefficient\cite{bib16} (iii) cosine similarity (iv) hub promoted index\cite{bib17} (v) hub depressed index (vi) Adamic Adar index \cite{bib18} (vii) Preferential attachment\cite{bib19} (viii) Resource allocation\cite{bib20} (ix) Leicht-Holme-Nerman Index \cite{bib21}. Similarly using path-based metrics one can compute paths between two nodes as similarity between node pairs. The methods are:
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.