Show simple item record

dc.contributor.authorWang, Jun
dc.contributor.authorYang, Ziying
dc.contributor.authorDomeniconi, Carlotta
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorYu, Guoxian
dc.date.accessioned2020-03-01T08:25:54Z
dc.date.available2020-03-01T08:25:54Z
dc.date.issued2020-02-26
dc.date.submitted2019-11-04
dc.identifier.citationWang, 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.doi10.1093/bib/bbz167
dc.identifier.urihttp://hdl.handle.net/10754/661819
dc.description.abstractDiscovering 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.sponsorshipNatural 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.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbz167/5758040
dc.rightsThis 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.titleCooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways.
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.journalBriefings in bioinformatics
dc.rights.embargodate2021-02-28
dc.eprint.versionPost-print
dc.contributor.institutionCollege of Computer and Information Sciences, Southwest University.
dc.contributor.institutionDepartment of Computer Science, George Mason University.
kaust.personZhang, Xiangliang
kaust.personYu, Guoxian
dc.date.accepted2019-12-13
refterms.dateFOA2020-03-01T08:50:06Z


Files in this item

Thumbnail
Name:
Bib_CDPathway.pdf
Size:
2.664Mb
Format:
PDF
Description:
Accepted manuscript

This item appears in the following Collection(s)

Show simple item record