HOODS: finding context-specific neighborhoods of proteins, chemicals and diseases

PeerJ. 2015 Jun 30;3:e1057. doi: 10.7717/peerj.1057. eCollection 2015.

Abstract

Clustering algorithms are often used to find groups relevant in a specific context; however, they are not informed about this context. We present a simple algorithm, HOODS, which identifies context-specific neighborhoods of entities from a similarity matrix and a list of entities specifying the context. We illustrate its applicability by finding disease-specific neighborhoods of functionally associated proteins, kinase-specific neighborhoods of structurally similar inhibitors, and physiological-system-specific neighborhoods of interconnected diseases. HOODS can be used via a simple interface at http://hoods.jensenlab.org, from where the source code can also be downloaded.

Keywords: Algorithm; Context-specific groups; Disease network; Kinase inhibitors; Neighborhoods; Protein interaction network; Small-molecule compounds.

Grant support

This work was in part funded by the Novo Nordisk Foundation Center for Protein Research [NNF14CC0001], by the TARGET research initiative (Danish Strategic Research Council [0603-00484B]), and by the National Institutes of Health [U54 CA189205-01]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.