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.
Original language | English |
---|---|
Article number | e1057 |
Journal | PeerJ |
Volume | 3 |
Pages (from-to) | 1-10 |
Number of pages | 10 |
ISSN | 2167-8359 |
DOIs | |
Publication status | Published - 2015 |