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dc.contributor.authorYu, Guoxian
dc.contributor.authorYang, Yeqian
dc.contributor.authorYan, Yangyang
dc.contributor.authorGuo, Maozu
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorWang, Jun
dc.date.accessioned2021-02-14T12:18:50Z
dc.date.available2021-02-14T12:18:50Z
dc.date.issued2021
dc.identifier.citationYu, G., Yang, Y., Yan, Y., Guo, M., Zhang, X., & Wang, J. (2021). DeepIDA: predicting isoform-disease associations by data fusion and deep neural networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1–1. doi:10.1109/tcbb.2021.3058801
dc.identifier.issn2374-0043
dc.identifier.doi10.1109/TCBB.2021.3058801
dc.identifier.urihttp://hdl.handle.net/10754/667398
dc.description.abstractAlternative splicing produces different isoforms from the same gene locus. Although the prediction of gene(miRNA)-disease associations have been extensively studied, few (or no) computational solutions have been proposed for the prediction of isoform-disease association (IDA) at a large scale, mainly due to the lack of disease annotations of isoforms. However, increasing evidences confirm the close connections between diseases and isoforms, which can more precisely uncover the pathology of complex diseases. Therefore, it is highly desirable to predict IDAs. To bridge this gap, we propose a deep neural network based solution (DeepIDA) to fuse multi-type genomics and transcriptomics data to predict IDAs. Particularly, DeepIDA uses gene-isoform relations to dispatch gene-disease associations to isoforms. In addition, it utilizes two DNN sub-networks with different structures to capture nucleotide and expression features of isoforms, Gene Ontology data and miRNA target data, respectively. After that, these two sub-networks are merged in a dense layer to predict IDAs. The experimental results on public datasets show that DeepIDA can effectively predict IDAs with AUPRC of 0.9141 and macro F-measure of 0.9155, which are much higher than those of competitive methods. Further study on sixteen isoform-disease association cases again corroborate the superiority of DeepIDA.
dc.description.sponsorshipThis work is supported by Natural Science Foundation of China (61872300, 62031003 and 62072380), and Qilu Scholarship of Shandong University. G. Yu and Y. Yang are with the School of Software and the Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan 250101, China.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9353272/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9353272
dc.rights(c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectIsoform-disease associations
dc.subjectAlternative splicing
dc.subjectDeep Neural Networks
dc.subjectData fusion
dc.subjectClass imbalance
dc.titleDeepIDA: predicting isoform-disease associations by data fusion and deep neural networks
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.journalIEEE/ACM Transactions on Computational Biology and Bioinformatics
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Software, Shandong University, 12589 Jinan, Shandong, China,
dc.contributor.institutionCollege of Computer and Information Sciences, Southwest University, 26463 Chongqing, Sichuan, China,
dc.contributor.institutionSchool of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, 117781 Beijing, Beijing, China,
dc.contributor.institutionJoint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, 12589 Jinan, Shandong, China, 250100
dc.identifier.pages1-1
kaust.personZhang, Xiangliang
refterms.dateFOA2021-02-15T05:49:08Z


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