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

## TL;DR

HOODS is a new algorithm that finds groups of related proteins, chemicals, and diseases based on a specific context, helping researchers understand biological relationships better.

## Contribution

HOODS introduces a novel method for identifying context-specific neighborhoods of biological entities using a similarity matrix and a context list.

## Key findings

- HOODS successfully identifies disease-specific neighborhoods of functionally related proteins.
- The algorithm finds kinase-specific neighborhoods of structurally similar inhibitors.
- HOODS reveals physiological-system-specific neighborhoods of interconnected diseases.

## 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.