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    DMIL-III: Isoform-isoform interaction prediction using deep multi-instance learning method

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    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Zeng, Jie
    Yu, Guoxian
    Wang, Jun
    Guo, Maozu
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/661878
    
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    Abstract
    Alternative splicing modulates protein-protein and other ligand interactions, it results in proteoforms, translated from isoforms that are alternatively spliced from the same gene, to interact with different partners and have distinct or even opposing functions. Therefore, systematically identifying protein-protein interaction at the isoform-level is crucial to explore the function of proteoforms. Constructing the isoform-level interaction network currently is prohibited by the lack of a large golden set of experimentally validated interacting isoforms, which enable computationally predicting isoform-isoform interactions. In this paper, a deep convolution neural network based multi-instance learning approach called DMIL-III is proposed to predict isoform interactions. DMIL-III takes a gene pair as `bag' and two isoforms of the pairwise genes as the `instance' of the bag. DMIL-III follows the principle of multi-instance learning that at least one isoform-isoform interaction exists for a positive gene pair and none interacting isoforms occurs for a negative gene pair. DMIL-III integrates RNA-seq, nucleotide sequence, domain-domain interaction and exon array data. Experimental results indicate that DMIL-III achieves a superior performance with Accuracy of 93% on single-instance gene bags and of 94% on multi-instance gene bags, which are at least 14% and 29% higher than those of state-of-the-art methods. In addition, we further test DMIL-III on a set of experimentally confirmed isoform-isoform interactions and obtain an Accuracy of 65%, which is at least 10% higher than those of comparing methods at the isoform-level. All these results show the effectiveness of DMIL-III for predicting isoform-isoform interactions.
    Citation
    Zeng, J., Yu, G., Wang, J., Guo, M., & Zhang, X. (2019). DMIL-III: Isoform-isoform interaction prediction using deep multi-instance learning method. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). doi:10.1109/bibm47256.2019.8982956
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
    DOI
    10.1109/BIBM47256.2019.8982956
    Additional Links
    https://ieeexplore.ieee.org/document/8982956/
    https://ieeexplore.ieee.org/document/8982956/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8982956
    ae974a485f413a2113503eed53cd6c53
    10.1109/BIBM47256.2019.8982956
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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