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dc.contributor.authorJosep Fabregó, Marc
dc.contributor.authorTeruel García, Xavier
dc.contributor.authorGiménez Ábalos, Víctor
dc.contributor.authorCirilo, Davide
dc.contributor.authorGarcia Gasulla, Dario
dc.contributor.authorÁlvarez Napagao, Sergio
dc.contributor.authorGarcia Gasulla, Marta
dc.contributor.authorAyguadé Parra, Eduard
dc.contributor.authorValencia, Alfonso
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.identifier.citationJosep, M. [et al.]. GOPHER, an HPC framework for large scale graph exploration and inference. A: International Conference on High Performance Computing. "High Performance Computing: ISC High Performance 2020 International Workshops: Frankfurt, Germany, June 21–25, 2020: revised selected papers". Berlín: Springer, 2020, p. 211-222. ISBN 978-3-030-59851-8. DOI 10.1007/978-3-030-59851-8_13.
dc.description.abstractBiological ontologies, such as the Human Phenotype Ontology (HPO) and the Gene Ontology (GO), are extensively used in biomedical research to investigate the complex relationship that exists between the phenome and the genome. The interpretation of the encoded information requires methods that efficiently interoperate between multiple ontologies providing molecular details of disease-related features. To this aim, we present GenOtype PHenotype ExplOrer (GOPHER), a framework to infer associations between HPO and GO terms harnessing machine learning and large-scale parallelism and scalability in High-Performance Computing. The method enables to map genotypic features to phenotypic features thus providing a valid tool for bridging functional and pathological annotations. GOPHER can improve the interpretation of molecular processes involved in pathological conditions, displaying a vast range of applications in biomedicine.
dc.description.sponsorshipThis work has been developed with the support of the Severo Ochoa Program (SEV-2015-0493); the Spanish Ministry of Science and Innovation (TIN2015- 65316-P); and the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement.
dc.format.extent12 p.
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshOntologies (Information retrieval)
dc.subject.lcshMachine learning
dc.subject.otherBiological ontologies
dc.subject.otherGraph exploration
dc.titleGOPHER, an HPC framework for large scale graph exploration and inference
dc.typeConference report
dc.subject.lemacOntologies (Informàtica)
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
local.citation.authorJosep, M.; Teruel, X.; Giménez, V.; Cirilo, D.; Garcia-Gasulla, D.; Álvarez-Napagao, S.; García, M.; Ayguadé, E.; Valencia, A.
local.citation.contributorInternational Conference on High Performance Computing
local.citation.publicationNameHigh Performance Computing: ISC High Performance 2020 International Workshops: Frankfurt, Germany, June 21–25, 2020: revised selected papers

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