Analyzing large biological datasets with association networks.

ABSTRACT: Due to advances in high-throughput biotechnologies biological information is being collected in databases at an amazing rate, requiring novel computational approaches that process collected data into new knowledge in a timely manner. In this study, we propose a computational framework for discovering modular structure, relationships and regularities in complex data. The framework utilizes a semantic-preserving vocabulary to convert records of biological annotations of an object, such as an organism, gene, chemical or sequence, into networks (Anets) of the associated annotations. An association between a pair of annotations in an Anet is determined by the similarity of their co-occurrence pattern with all other annotations in the data. This feature captures associations between annotations

SUBMITTER: Karpinets TV 

PROVIDER: S-EPMC3458522 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature