Selected Tools and Resources from the IDSL
The following tools and resources are some of the most widely used from prior IDSL projects. Additional tools and resources include the T2DM-NET knowledge network for diabetes, the NCATS Phenotypic Drug Discovery Resource, and the ChemBioSpace drug/gene/disease/side-effect association finding tool
SEMANTIC LINK ASSOCIATION PREDICTION (SLAP)
SLAP is a tool that will profile drugs against targets (and vice versa) using semantically linked networks of information on compounds, genes, pathways, and related information. It can be used in numerous ways including predicting on- and off-target interactions, drug repurposing, and identification of mechanisms of action. For more information, see our PLoS Compuational Biology paper. You can also try the tool by clicking the link to the left. A related tool call SEMAP is available for commercial use from Data2Discovery Inc.
Chem2Bio2RDF is a demonstration of the power of semantically linked data. It shows how numerous publicly available biomedical datasets can be linked together and used to answer important biomedical questions that would otherwise be very difficult to answer, such as identifying multiple pathway inhibitors and associating drugs with particular side effects. You can read more in our BMC Bioinformatics paper, or access the resource by clicking the link to the left. Note that this was a proof-of-concept project and the data is not updated. However, Chem2Bio2RDF was drawn into the OpenPHACTS project which maintains frequently updated data.
NetPredictor is an open source R package for prediction of missing links in any given bipartite network. The package provides utilities to compute missing links in bipartite and unipartite networks using Random Walk with Restart and a network inference algorithm. 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. It includes an example application written in R-Shiny for prediction of drug-target associations. You can read more in our bioRxiv paper, or access source code and documentation in the github repository by clicking the image to the left.