• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data.

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    A Siamese Neural Network model for the prioritization of metabolic disorders by integrating real and simulated data.pdf
    Size:
    380.8Kb
    Format:
    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2021-12-31
    Download
    Type
    Article
    Authors
    Messa, Gian Marco
    Napolitano, Francesco
    Elsea, Sarah H
    di Bernardo, Diego
    Gao, Xin cc
    KAUST Department
    Computer Science
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Computational Bioscience Research Center (CBRC)
    KAUST Grant Number
    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
    REI/1/0018-01-01
    URF/1/3450-01
    Date
    2020-12-31
    Embargo End Date
    2021-12-31
    Permanent link to this record
    http://hdl.handle.net/10754/666798
    
    Metadata
    Show full item record
    Abstract
    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/btaa841
    Sponsors
    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)
    DOI
    10.1093/bioinformatics/btaa841
    PubMed ID
    33381827
    Additional Links
    https://academic.oup.com/bioinformatics/article/36/Supplement_2/i787/6055915
    ae974a485f413a2113503eed53cd6c53
    10.1093/bioinformatics/btaa841
    Scopus Count
    Collections
    Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

    Related articles

    • Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification
    • Authors: Wolahan SM, Hirt D, Glenn TC, Kobeissy FH
    • Issue date: 2015
    • The role of the Human Metabolome Database in inborn errors of metabolism.
    • Authors: Mandal R, Chamot D, Wishart DS
    • Issue date: 2018 May
    • metPropagate: network-guided propagation of metabolomic information for prioritization of metabolic disease genes.
    • Authors: Graham Linck EJ, Richmond PA, Tarailo-Graovac M, Engelke U, Kluijtmans LAJ, Coene KLM, Wevers RA, Wasserman W, van Karnebeek CDM, Mostafavi S
    • Issue date: 2020
    • Metabolomics: a challenge for detecting and monitoring inborn errors of metabolism.
    • Authors: Mussap M, Zaffanello M, Fanos V
    • Issue date: 2018 Sep
    • A compendium of inborn errors of metabolism mapped onto the human metabolic network.
    • Authors: Sahoo S, Franzson L, Jonsson JJ, Thiele I
    • Issue date: 2012 Oct
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.