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dc.contributor.authorMessa, Gian Marco
dc.contributor.authorNapolitano, Francesco
dc.contributor.authorElsea, Sarah H
dc.contributor.authordi Bernardo, Diego
dc.contributor.authorGao, Xin
dc.date.accessioned2021-01-03T13:04:51Z
dc.date.available2021-01-03T13:04:51Z
dc.date.issued2020-12-31
dc.identifier.citationMessa, G. M., Napolitano, F., Elsea, S. H., di Bernardo, D., & Gao, X. (2020). A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data. Bioinformatics, 36(Supplement_2), i787–i794. doi:10.1093/bioinformatics/btaa841
dc.identifier.issn1367-4803
dc.identifier.pmid33381827
dc.identifier.doi10.1093/bioinformatics/btaa841
dc.identifier.urihttp://hdl.handle.net/10754/666798
dc.description.abstractMotivationUntargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Computational approaches, such as metabolic network simulations and machine learning, could significantly help to exploit metabolomic data to aid the diagnostic process. While the former suffers from limited predictive accuracy, the latter is normally able to generalize only to IEMs for which sufficient data are available. Here, we propose a hybrid approach that exploits the best of both worlds by building a mapping between simulated and real metabolic data through a novel method based on Siamese neural networks (SNN).ResultsThe proposed SNN model is able to perform disease prioritization for the metabolic profiles of IEM patients even for diseases that it was not trained to identify. To the best of our knowledge, this has not been attempted before. The developed model is able to significantly outperform a baseline model that relies on metabolic simulations only. The prioritization performances demonstrate the feasibility of the method, suggesting that the integration of metabolic models and data could significantly aid the IEM diagnosis process in the near future.Availability and implementationMetabolic datasets used in this study are publicly available from the cited sources. The original data produced in this study, including the trained models and the simulated metabolic profiles, are also publicly available (Messa et al., 2020).
dc.description.sponsorshipThe research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [FCC/1/1976-04, FCC/1/1976-06, FCC/1/1976-17, FCC/1/1976-18, FCC/1/1976-23, FCC/1/1976-25, FCC/1/1976-26, URF/1/3450-01, URF/1/4098-01-01 and REI/1/0018-01-01].
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/article/36/Supplement_2/i787/6055915
dc.rightsThis is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics (Oxford, England) following peer review. The version of record is available online at: https://academic.oup.com/bioinformatics/article/36/Supplement_2/i787/6055915.
dc.titleA Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data.
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalBioinformatics (Oxford, England)
dc.rights.embargodate2021-12-31
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
dc.contributor.institutionTelethon Institute of Genetics and Medicine (TIGEM), Pozzuoli 80078, Italy
dc.contributor.institutionDepartment of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, 80125 Naples, Italy
dc.identifier.volume36
dc.identifier.issueSupplement_2
dc.identifier.pagesi787-i794
kaust.personMessa, Gian Marco
kaust.personNapolitano, Francesco
kaust.personGao, Xin
kaust.grant.numberFCC/1/1976-04
kaust.grant.numberFCC/1/1976-06
kaust.grant.numberFCC/1/1976-17
kaust.grant.numberFCC/1/1976-18
kaust.grant.numberFCC/1/1976-23
kaust.grant.numberFCC/1/1976-25
kaust.grant.numberFCC/1/1976-26
kaust.grant.numberREI/1/0018-01-01
kaust.grant.numberURF/1/3450-01
refterms.dateFOA2021-01-04T06:46:08Z
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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