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    Differentiating isoform functions with collaborative matrix factorization.

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    DisoFun.pdf
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    Accepted manuscript
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    Type
    Article
    Authors
    Wang, Keyao
    Wang, Jun
    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
    2020-03-17
    Embargo End Date
    2021-03-17
    Submitted Date
    2019-07-01
    Permanent link to this record
    http://hdl.handle.net/10754/662248
    
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    Abstract
    MOTIVATION:Isoforms are alternatively spliced mRNAs of genes. They can be translated into different functional proteoforms, and thus greatly increase the functional diversity of protein variants (or proteoforms). Differentiating the functions of isoforms (or proteoforms) helps understanding the underlying pathology of various complex diseases at a deeper granularity. Since existing functional genomic databases uniformly record the annotations at the gene-level, and rarely record the annotations at the isoform-level, differentiating isoform functions is more challenging than the traditional gene-level function prediction. RESULTS:Several approaches have been proposed to differentiate the functions of isoforms. They generally follow the multi-instance learning paradigm by viewing each gene as a bag and the spliced isoforms as its instances, and push functions of bags onto instances. These approaches implicitly assume the collected annotations of genes are complete and only integrate multiple RNA-seq datasets. As such, they have compromised performance. We propose a data integrative solution (called DisoFun) to Differentiate isoform Functions with collaborative matrix factorization. DisoFun assumes the functional annotations of genes are aggregated from those of key isoforms. It collaboratively factorizes the isoform data matrix and gene-term data matrix (storing Gene Ontology annotations of genes) into low-rank matrices to simultaneously explore the latent key isoforms, and achieve function prediction by aggregating predictions to their originating genes. In addition, it leverages the PPI network and Gene Ontology structure to further coordinate the matrix factorization. Extensive experimental results show that DisoFun improves the area under the receiver operating characteristic curve and area under the precision-recall curve of existing solutions by at least 7.7 and 28.9%, respectively. We further investigate DisoFun on four exemplar genes (LMNA, ADAM15, BCL2L1 and CFLAR) with known functions at the isoform-level, and observed that DisoFun can differentiate functions of their isoforms with 90.5% accuracy. AVAILABILITY AND IMPLEMENTATION:The code of DisoFun is available at mlda.swu.edu.cn/codes.php?name=DisoFun. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
    Citation
    Wang, K., Wang, J., Domeniconi, C., Zhang, X., & Yu, G. (2019). Differentiating isoform functions with collaborative matrix factorization. Bioinformatics. doi:10.1093/bioinformatics/btz847
    Sponsors
    This work was supported by National Natural Science Foundation of China [61872300, 61873214]; Fundamental Research Funds for the Central Universities [XDJK2019B024]; and Natural Science Foundation of CQ CSTC [cstc2018jcyjAX0228].
    Publisher
    Oxford University Press (OUP)
    Journal
    Bioinformatics
    DOI
    10.1093/bioinformatics/btz847
    Additional Links
    https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz847/5625622
    ae974a485f413a2113503eed53cd6c53
    10.1093/bioinformatics/btz847
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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