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    Isoform-Disease Association Prediction by Data Fusion

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    Type
    Conference Paper
    Authors
    Huang, Qiuyue
    Wang, Jun
    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
    2020-08-18
    Online Publication Date
    2020-08-18
    Print Publication Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/665195
    
    Metadata
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    Abstract
    Alternative splicing enables a gene spliced into different isoforms, which are closely related with diverse developmental abnormalities. Identifying the isoform-disease associations helps to uncover the underlying pathology of various complex diseases, and to develop precise treatments and drugs for these diseases. Although many approaches have been proposed for predicting gene-disease associations and isoform functions, few efforts have been made toward predicting isoform-disease associations in large-scale, the main bottleneck is the lack of ground-truth isoform-disease associations. To bridge this gap, we propose a multi-instance learning inspired computational approach called IDAPred to fuse genomics and transcriptomics data for isoform-disease association prediction. Given the bag-instance relationship between gene and its spliced isoforms, IDAPred introduces a dispatch and aggregation term to dispatch gene-disease associations to individual isoforms, and reversely aggregate these dispatched associations to affiliated genes. Next, it fuses different genomics and transcriptomics data to replenish gene-disease associations and to induce a linear classifier for predicting isoform-disease associations in a coherent way. In addition, to alleviate the bias toward observed gene-disease associations, it adds a regularization term to differentiate the currently observed associations from the unobserved (potential) ones. Experimental results show that IDAPred significantly outperforms the related state-of-the-art methods.
    Citation
    Huang, Q., Wang, J., Zhang, X., & Yu, G. (2020). Isoform-Disease Association Prediction by Data Fusion. Lecture Notes in Computer Science, 44–55. doi:10.1007/978-3-030-57821-3_5
    Sponsors
    This research is supported by NSFC (61872300), Fundamental Research Funds for the Central Universities (XDJK2019B024 and XDJK2020B028), Natural Science Foundation of CQ CSTC (cstc2018jcyjAX0228).
    Publisher
    Springer Nature
    Conference/Event name
    16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020
    ISBN
    9783030578206
    DOI
    10.1007/978-3-030-57821-3_5
    Additional Links
    http://link.springer.com/10.1007/978-3-030-57821-3_5
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-57821-3_5
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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