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dc.contributor.authorHuang, Qiuyue
dc.contributor.authorWang, Jun
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorGuo, Maozu
dc.contributor.authorYu, Guoxian
dc.date.accessioned2021-04-11T08:30:00Z
dc.date.available2021-04-11T08:30:00Z
dc.date.issued2021-04-07
dc.identifier.citationHuang, Q., Wang, J., Zhang, X., Guo, M., & Yu, G. (2021). IsoDA: Isoform–Disease Association Prediction by Multiomics Data Fusion. Journal of Computational Biology. doi:10.1089/cmb.2020.0626
dc.identifier.issn1066-5277
dc.identifier.pmid33826865
dc.identifier.doi10.1089/cmb.2020.0626
dc.identifier.urihttp://hdl.handle.net/10754/668633
dc.description.abstractA gene can be spliced into different isoforms by alternative splicing, which contributes to the functional diversity of protein species. Computational prediction of gene-disease associations (GDAs) has been studied for decades. However, the process of identifying the isoform-disease associations (IDAs) at a large scale is rarely explored, which can decipher the pathology at a more granular level. The main bottleneck is the lack of IDAs in current databases and the multilevel omics data fusion. To bridge this gap, we propose a computational approach called Isoform-Disease Association prediction by multiomics data fusion (IsoDA) to predict IDAs. Based on the relationship between a gene and its spliced isoforms, IsoDA first introduces a dispatch and aggregation term to dispatch gene-disease associations to individual isoforms, and reversely aggregate these dispatched associations to their hosting genes. At the same time, it fuses the genome, transcriptome, and proteome data by joint matrix factorization to improve the prediction of IDAs. Experimental results show that IsoDA significantly outperforms the related state-of-the-art methods at both the gene level and isoform level. A case study further shows that IsoDA credibly identifies three isoforms spliced from apolipoprotein E, which have individual associations with Alzheimer's disease, and two isoforms spliced from vascular endothelial growth factor A, which have different associations with coronary heart disease. The codes of IsoDA are available at http://mlda.swu.edu.cn/codes.php?name=IsoDA.
dc.publisherMary Ann Liebert Inc
dc.relation.urlhttps://www.liebertpub.com/doi/10.1089/cmb.2020.0626
dc.rightsArchived with thanks to Journal of computational biology : a journal of computational molecular cell biology
dc.titleIsoDA: Isoform-Disease Association Prediction by Multiomics Data Fusion.
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.identifier.journalJournal of computational biology : a journal of computational molecular cell biology
dc.rights.embargodate2022-04-07
dc.eprint.versionPost-print
dc.contributor.institutionCollege of Computer and Information Science, Southwest University, Chongqing, China.
dc.contributor.institutionSchool of Software, Shandong University, Jinan, China.
dc.contributor.institutionDepartment of Computer Science, College of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.
kaust.personZhang, Xiangliang
kaust.personYu, Guoxian
refterms.dateFOA2021-04-11T12:59:08Z


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