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RecRWR: a recursive random walk method for improved identification of diseases.

Arrais JP, Oliveira JL - Biomed Res Int (2015)

Bottom Line: High-throughput methods such as next-generation sequencing or DNA microarrays lack precision, as they return hundreds of genes for a single disease profile.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Informatics Engineering (DEI), Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, 3030-290 Coimbra, Portugal.

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.

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Related in: MedlinePlus

Pseudocode for the RecRWR method.
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getmorefigures.php?uid=PMC4385608&req=5

alg1: Pseudocode for the RecRWR method.

Mentions: Next we formulate the RecRWR algorithm including a detailed pseudocode description of the algorithm (Algorithm 1). The three main components are


RecRWR: a recursive random walk method for improved identification of diseases.

Arrais JP, Oliveira JL - Biomed Res Int (2015)

Pseudocode for the RecRWR method.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4385608&req=5

alg1: Pseudocode for the RecRWR method.
Mentions: Next we formulate the RecRWR algorithm including a detailed pseudocode description of the algorithm (Algorithm 1). The three main components are

Bottom Line: High-throughput methods such as next-generation sequencing or DNA microarrays lack precision, as they return hundreds of genes for a single disease profile.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Informatics Engineering (DEI), Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, 3030-290 Coimbra, Portugal.

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.

Show MeSH
Related in: MedlinePlus