RecRWR: A Recursive Random Walk Method for Improved Identification of Diseases

Authors

Abstract

High-throughput methods such as next-generation sequencing or DNA microarrays lack precision, as they return hundreds of genes for a single disease profile. Several computational methods applied to physical interaction of protein networks have been successfully used in identification of the best disease candidates for each expression profile. An open problem for these methods is the ability to combine and take advantage of the wealth of biomedical data publicly available. We propose an enhanced method to improve selection of the best disease targets for a multilayer biomedical network that integrates PPI data annotated with stable knowledge from OMIM diseases and GO biological processes. We present a comprehensive validation that demonstrates the advantage of the proposed approach, Recursive Random Walk with Restarts (RecRWR). The obtained results outline the superiority of the proposed approach, RecRWR, in identifying disease candidates, especially with high levels of biological noise and benefiting from all data available.

Keywords

Random Walk

Subject

Bioinformatics

Journal

BioMed research international, Hindawi Publishing Corporation 2015

DOI

Cited by

Year 2015 : 1 citations

Yuan, Jie, et al. "Predicting disease genes based on normalized protein modules and phenotype ontology." Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on. IEEE, 2015.