A computational method based on the integration of heterogeneous networks for predicting disease-gene associations.

ABSTRACT: The identification of disease-causing genes is a fundamental challenge in human health and of great importance in improving medical care, and provides a better understanding of gene functions. Recent computational approaches based on the interactions among human proteins and disease similarities have shown their power in tackling the issue. In this paper, a novel systematic and global method that integrates two heterogeneous networks for prioritizing candidate disease-causing genes is provided, based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein interactions. In this method, the association score function between a query disease and a candidate gene is defined as the weighted sum of all the association sc

SUBMITTER: Guo X 

PROVIDER: S-EPMC3166294 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature