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Title: RecRWR: A Recursive Random Walk Method for Improved Identification of Diseases
Authors: Arrais, Joel Perdiz 
Oliveira, José Luís 
Issue Date: 2014
Publisher: Hindawi Publishing Corporation
Citation: ARRAIS, Joel Perdiz; OLIVEIRA, José Luís - RecRWR: A Recursive Random Walk Method for Improved Identification of Diseases. "BioMed Research International". ISSN 2314-6141. (2014)
Serial title, monograph or event: BioMed Research International
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.
ISSN: 2314-6141
DOI: 10.1155/2015/747156
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
FCTUC Eng.Informática - Artigos em Revistas Internacionais

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