A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data.
KAUST DepartmentComputer Science
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
KAUST Grant NumberFCC/1/1976-04
Embargo End Date2021-12-31
Permanent link to this recordhttp://hdl.handle.net/10754/666798
MetadataShow full item record
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).
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
SponsorsThe 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].
PublisherOxford University Press (OUP)
JournalBioinformatics (Oxford, England)
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