dc.contributor.author Wang, Jun dc.contributor.author Yang, Ziying dc.contributor.author Domeniconi, Carlotta dc.contributor.author Zhang, Xiangliang dc.contributor.author Yu, Guoxian dc.date.accessioned 2020-03-01T08:25:54Z dc.date.available 2020-03-01T08:25:54Z dc.date.issued 2020-02-26 dc.date.submitted 2019-11-04 dc.identifier.citation Wang, J., Yang, Z., Domeniconi, C., Zhang, X., & Yu, G. (2020). Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways. Briefings in Bioinformatics. doi:10.1093/bib/bbz167 dc.identifier.doi 10.1093/bib/bbz167 dc.identifier.uri http://hdl.handle.net/10754/661819 dc.description.abstract Discovering driver pathways is an essential step to uncover the molecular mechanism underlying cancer and to explore precise treatments for cancer patients. However, due to the difficulties of mapping genes to pathways and the limited knowledge about pathway interactions, most previous work focus on identifying individual pathways. In practice, two (or even more) pathways interplay and often cooperatively trigger cancer. In this study, we proposed a new approach called CDPathway to discover cooperative driver pathways. First, CDPathway introduces a driver impact quantification function to quantify the driver weight of each gene. CDPathway assumes that genes with larger weights contribute more to the occurrence of the target disease and identifies them as candidate driver genes. Next, it constructs a heterogeneous network composed of genes, miRNAs and pathways nodes based on the known intra(inter)-relations between them and assigns the quantified driver weights to gene-pathway and gene-miRNA relational edges. To transfer driver impacts of genes to pathway interaction pairs, CDPathway collaboratively factorizes the weighted adjacency matrices of the heterogeneous network to explore the latent relations between genes, miRNAs and pathways. After this, it reconstructs the pathway interaction network and identifies the pathway pairs with maximal interactive and driver weights as cooperative driver pathways. Experimental results on the breast, uterine corpus endometrial carcinoma and ovarian cancer data from The Cancer Genome Atlas show that CDPathway can effectively identify candidate driver genes [area under the receiver operating characteristic curve (AUROC) of $\geq$0.9] and reconstruct the pathway interaction network (AUROC of>0.9), and it uncovers much more known (potential) driver genes than other competitive methods. In addition, CDPathway identifies 150% more driver pathways and 60% more potential cooperative driver pathways than the competing methods. The code of CDPathway is available at http://mlda.swu.edu.cn/codes.php?name=CDPathway. dc.description.sponsorship Natural Science Foundation of China (61873214 and 61872300); Fundamental Research Funds for the Central Universities (XDJK2020B028 and XDJK2019B024); Natural Science Foundation of CQ CSTC (cstc2018jcyjAX0228); fund from King Abdullah University of Science and Technology (FCC/1/1976-19-01). dc.publisher Oxford University Press (OUP) dc.relation.url https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbz167/5758040 dc.rights This is a pre-copyedited, author-produced PDF of an article accepted for publication in Briefings in bioinformatics following peer review. The version of record is available online at: https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbz167/5758040. dc.title Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways. dc.type Article dc.contributor.department Computer Science Program dc.contributor.department Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division dc.contributor.department Machine Intelligence & kNowledge Engineering Lab dc.identifier.journal Briefings in bioinformatics dc.rights.embargodate 2021-02-28 dc.eprint.version Post-print dc.contributor.institution College of Computer and Information Sciences, Southwest University. dc.contributor.institution Department of Computer Science, George Mason University. kaust.person Zhang, Xiangliang kaust.person Yu, Guoxian dc.date.accepted 2019-12-13 refterms.dateFOA 2020-03-01T08:50:06Z
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