• Login
    View Item 
    •   Home
    • Research
    • Datasets
    • View Item
    •   Home
    • Research
    • Datasets
    • 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 LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    MOESM3 of Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Data File
    Authors
    Fan, Ming
    Xia, Pingping
    Liu, Bin
    Zhang, Lin
    Wang, Yue
    Gao, Xin cc
    Li, Lihua
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Structural and Functional Bioinformatics Group
    Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/664821
    
    Metadata
    Show full item record
    Abstract
    Additional file 3: Figure S2. Image features from the slow-flow kinetics subregion correlated with gene modules.
    Citation
    Fan, M., Pingping Xia, Liu, B., Zhang, L., Wang, Y., Gao, X., & Lihua Li. (2019). MOESM3 of Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients. Figshare. https://doi.org/10.6084/M9.FIGSHARE.10001720
    Publisher
    figshare
    DOI
    10.6084/m9.figshare.10001720
    Relations
    Is Supplement To:
    • [Article]
      Fan, M., Xia, P., Liu, B., Zhang, L., Wang, Y., Gao, X., & Li, L. (2019). Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients. Breast Cancer Research, 21(1). doi:10.1186/s13058-019-1199-8. DOI: 10.1186/s13058-019-1199-8 HANDLE: 10754/659544
    ae974a485f413a2113503eed53cd6c53
    10.6084/m9.figshare.10001720
    Scopus Count
    Collections
    Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Datasets; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    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.