• 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

    Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways.

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Bib_CDPathway.pdf
    Size:
    2.664Mb
    Format:
    PDF
    Description:
    Accepted manuscript
    Download
    Type
    Article
    Authors
    Wang, Jun
    Yang, Ziying
    Domeniconi, Carlotta
    Zhang, Xiangliang cc
    Yu, Guoxian
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2020-02-26
    Embargo End Date
    2021-02-28
    Submitted Date
    2019-11-04
    Permanent link to this record
    http://hdl.handle.net/10754/661819
    
    Metadata
    Show full item record
    Abstract
    Discovering driver pathways is an essential step to uncover the molecular mechanism underlying cancer and to explore precise treatments for cancer patients. However, due to the difficulties of mapping genes to pathways and the limited knowledge about pathway interactions, most previous work focus on identifying individual pathways. In practice, two (or even more) pathways interplay and often cooperatively trigger cancer. In this study, we proposed a new approach called CDPathway to discover cooperative driver pathways. First, CDPathway introduces a driver impact quantification function to quantify the driver weight of each gene. CDPathway assumes that genes with larger weights contribute more to the occurrence of the target disease and identifies them as candidate driver genes. Next, it constructs a heterogeneous network composed of genes, miRNAs and pathways nodes based on the known intra(inter)-relations between them and assigns the quantified driver weights to gene-pathway and gene-miRNA relational edges. To transfer driver impacts of genes to pathway interaction pairs, CDPathway collaboratively factorizes the weighted adjacency matrices of the heterogeneous network to explore the latent relations between genes, miRNAs and pathways. After this, it reconstructs the pathway interaction network and identifies the pathway pairs with maximal interactive and driver weights as cooperative driver pathways. Experimental results on the breast, uterine corpus endometrial carcinoma and ovarian cancer data from The Cancer Genome Atlas show that CDPathway can effectively identify candidate driver genes [area under the receiver operating characteristic curve (AUROC) of $\geq $0.9] and reconstruct the pathway interaction network (AUROC of>0.9), and it uncovers much more known (potential) driver genes than other competitive methods. In addition, CDPathway identifies 150% more driver pathways and 60% more potential cooperative driver pathways than the competing methods. The code of CDPathway is available at http://mlda.swu.edu.cn/codes.php?name=CDPathway.
    Citation
    Wang, J., Yang, Z., Domeniconi, C., Zhang, X., & Yu, G. (2020). Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways. Briefings in Bioinformatics. doi:10.1093/bib/bbz167
    Sponsors
    Natural Science Foundation of China (61873214 and 61872300); Fundamental Research Funds for the Central Universities (XDJK2020B028 and XDJK2019B024); Natural Science Foundation of CQ CSTC (cstc2018jcyjAX0228); fund from King Abdullah University of Science and Technology (FCC/1/1976-19-01).
    Publisher
    Oxford University Press (OUP)
    Journal
    Briefings in bioinformatics
    DOI
    10.1093/bib/bbz167
    Additional Links
    https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbz167/5758040
    ae974a485f413a2113503eed53cd6c53
    10.1093/bib/bbz167
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    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.