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dc.contributor.authorQu, Sisi
dc.contributor.authorXu, Mengmeng
dc.contributor.authorGhanem, Bernard
dc.contributor.authorTegner, Jesper
dc.date.accessioned2020-07-27T13:06:25Z
dc.date.available2020-07-27T13:06:25Z
dc.date.issued2020-07-10
dc.identifier.citationPresented at the ICML 2020 Workshop on Computational Biology (WCB)
dc.identifier.urihttp://hdl.handle.net/10754/664433
dc.description.abstractNetworks are abundant in the life sciences. Outstanding challenges include how to characterize similarities between networks, and in extension how to integrate information across networks. Yet, network alignment remains a core algorithmic problem. Here, we present a novel learning algorithm called evolutionary heat diffusion-based network alignment (EDNA) to address this challenge. EDNA uses the diffusion signal as a proxy for computing node similarities between networks. Comparing EDNA with state-of-the-art algorithms on a popular protein-protein interaction network dataset, using four different evaluation metrics, we achieve (i) the most accurate alignments, (ii) increased robustness against noise, and (iii) superior scaling capacity. The EDNA algorithm is versatile in that other available network alignments/embeddings can be used as an initial baseline alignment, and then EDNA works as a wrapper around them by running the evolutionary diffusion on top of them. In conclusion, EDNA outperforms state-of-the-art methods for network alignment, thus setting the stage for large-scale comparison and integration of networks.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2007.05401
dc.rightsArchived with thanks to arXiv
dc.titleLearning Heat Diffusion for Network Alignment
dc.typePreprint
dc.contributor.departmentBioengineering
dc.contributor.departmentBioengineering Program
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentVCC Analytics Research Group
dc.eprint.versionPre-print
dc.identifier.arxivid2007.05401
kaust.personQu, Sisi
kaust.personXu, Mengmeng
kaust.personGhanem, Bernard
kaust.personTegner, Jesper
refterms.dateFOA2020-07-27T13:07:45Z


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