A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data.
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A Siamese Neural Network model for the prioritization of metabolic disorders by integrating real and simulated data.pdf
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Accepted manuscript
Embargo End Date:
2021-12-31
Type
ArticleKAUST Department
Computer ScienceComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
KAUST Grant Number
FCC/1/1976-04FCC/1/1976-06
FCC/1/1976-17
FCC/1/1976-18
FCC/1/1976-23
FCC/1/1976-25
FCC/1/1976-26
REI/1/0018-01-01
URF/1/3450-01
Date
2020-12-31Embargo End Date
2021-12-31Permanent link to this record
http://hdl.handle.net/10754/666798
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MotivationUntargeted 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).Citation
Messa, 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/btaa841Sponsors
The 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].Publisher
Oxford University Press (OUP)Journal
Bioinformatics (Oxford, England)PubMed ID
33381827ae974a485f413a2113503eed53cd6c53
10.1093/bioinformatics/btaa841
Scopus Count
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