title: RecRWR: a recursive random walk method for improved identification of diseases.
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.
authors: Department of Informatics Engineering (DEI), Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, 3030-290 Coimbra, Portugal.
authors: Joel Perdiz
authors: JP
authors: Arrais
authors: Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal.
authors: José Luís
authors: JL
authors: Oliveira
mesh_terms: D030541:Databases, Genetic; D020869:Gene Expression Profiling; D030342:Genetic Diseases, Inborn / Q000145:classification / Q000175:diagnosis / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis