Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways.
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab
Embargo End Date2021-02-28
Permanent link to this recordhttp://hdl.handle.net/10754/661819
MetadataShow full item record
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.
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
SponsorsNatural 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).
PublisherOxford University Press (OUP)
JournalBriefings in bioinformatics