Network-based prediction and knowledge mining of disease genes.

ABSTRACT:

Background

In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules. Due to the interdependent nature of network entities, evaluating proteins in this context can reveal relationships that may not otherwise be evident.

Methods

We examined the human protein interaction network as it relates to human illness using the Disease Ontology. After calculating several topological metrics, we trained an alternating decision tree (ADTree) classifie

SUBMITTER: Carson MB 

PROVIDER: S-EPMC4460923 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature