RecRWR: A Recursive Random Walk Method for Improved Identification of Diseases
Identifieur interne : 000116 ( Main/Exploration ); précédent : 000115; suivant : 000117RecRWR: A Recursive Random Walk Method for Improved Identification of Diseases
Auteurs : Joel Perdiz Arrais [Portugal] ; José Luís Oliveira [Portugal]Source :
- BioMed Research International [ 2314-6133 ] ; 2015.
Descripteurs français
- KwdFr :
- Analyse de profil d'expression de gènes, Bases de données génétiques, Humains, Maladies génétiques congénitales (), Maladies génétiques congénitales (diagnostic), Maladies génétiques congénitales (génétique), Modèles génétiques, Séquençage nucléotidique à haut débit, Séquençage par oligonucléotides en batterie.
- MESH :
- diagnostic : Maladies génétiques congénitales.
- génétique : Maladies génétiques congénitales.
- Analyse de profil d'expression de gènes, Bases de données génétiques, Humains, Maladies génétiques congénitales, Modèles génétiques, Séquençage nucléotidique à haut débit, Séquençage par oligonucléotides en batterie.
English descriptors
- KwdEn :
- MESH :
- classification : Genetic Diseases, Inborn.
- diagnosis : Genetic Diseases, Inborn.
- genetics : Genetic Diseases, Inborn.
- Databases, Genetic, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Humans, Models, Genetic, Oligonucleotide Array Sequence Analysis.
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.
Url:
DOI: 10.1155/2015/747156
PubMed: 25874227
PubMed Central: 4385608
Affiliations:
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Le document en format XML
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Oliveira</name><affiliation wicri:level="1"><nlm:aff id="I2">Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal</nlm:aff><country xml:lang="fr">Portugal</country><wicri:regionArea>Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro</wicri:regionArea><wicri:noRegion>3810-193 Aveiro</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">25874227</idno><idno type="pmc">4385608</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4385608</idno><idno type="RBID">PMC:4385608</idno><idno type="doi">10.1155/2015/747156</idno><date when="2015">2015</date><idno type="wicri:Area/Pmc/Corpus">000437</idno><idno type="wicri:explorRef" wicri:stream="Pmc" 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type="wicri:doubleKey">2314-6133:2015:Perdiz Arrais J:recrwr:a:recursive</idno><idno type="wicri:Area/Main/Merge">000115</idno><idno type="wicri:Area/Main/Curation">000116</idno><idno type="wicri:Area/Main/Exploration">000116</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">RecRWR: A Recursive Random Walk Method for Improved Identification of Diseases</title><author><name sortKey="Perdiz Arrais, Joel" sort="Perdiz Arrais, Joel" uniqKey="Perdiz Arrais J" first="Joel" last="Perdiz Arrais">Joel Perdiz Arrais</name><affiliation wicri:level="1"><nlm:aff id="I1">Department of Informatics Engineering (DEI), Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, 3030-290 Coimbra, Portugal</nlm:aff><country xml:lang="fr">Portugal</country><wicri:regionArea>Department of Informatics Engineering (DEI), Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, 3030-290 Coimbra</wicri:regionArea><wicri:noRegion>3030-290 Coimbra</wicri:noRegion></affiliation></author><author><name sortKey="Oliveira, Jose Luis" sort="Oliveira, Jose Luis" uniqKey="Oliveira J" first="José Luís" last="Oliveira">José Luís Oliveira</name><affiliation wicri:level="1"><nlm:aff id="I2">Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal</nlm:aff><country xml:lang="fr">Portugal</country><wicri:regionArea>Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro</wicri:regionArea><wicri:noRegion>3810-193 Aveiro</wicri:noRegion></affiliation></author></analytic><series><title level="j">BioMed Research International</title><idno type="ISSN">2314-6133</idno><idno type="eISSN">2314-6141</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Databases, Genetic</term><term>Gene Expression Profiling</term><term>Genetic Diseases, Inborn (classification)</term><term>Genetic Diseases, Inborn (diagnosis)</term><term>Genetic Diseases, Inborn (genetics)</term><term>High-Throughput Nucleotide Sequencing</term><term>Humans</term><term>Models, Genetic</term><term>Oligonucleotide Array Sequence Analysis</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de profil d'expression de gènes</term><term>Bases de données génétiques</term><term>Humains</term><term>Maladies génétiques congénitales ()</term><term>Maladies génétiques congénitales (diagnostic)</term><term>Maladies génétiques congénitales (génétique)</term><term>Modèles génétiques</term><term>Séquençage nucléotidique à haut débit</term><term>Séquençage par oligonucléotides en batterie</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Genetic Diseases, Inborn</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Genetic Diseases, Inborn</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Maladies génétiques congénitales</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Genetic Diseases, Inborn</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Maladies génétiques congénitales</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Databases, Genetic</term><term>Gene Expression Profiling</term><term>High-Throughput Nucleotide Sequencing</term><term>Humans</term><term>Models, Genetic</term><term>Oligonucleotide Array Sequence Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse de profil d'expression de gènes</term><term>Bases de données génétiques</term><term>Humains</term><term>Maladies génétiques congénitales</term><term>Modèles génétiques</term><term>Séquençage nucléotidique à haut débit</term><term>Séquençage par oligonucléotides en batterie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Giallourakis, C" uniqKey="Giallourakis C">C. Giallourakis</name></author><author><name sortKey="Henson, C" uniqKey="Henson C">C. Henson</name></author><author><name sortKey="Reich, M" uniqKey="Reich M">M. Reich</name></author><author><name sortKey="Xie, X" uniqKey="Xie X">X. 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