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dc.contributor.authorDai, X.
dc.contributor.authorXu, F.
dc.contributor.authorWang, S.
dc.contributor.authorMundra, Piyushkumar A
dc.contributor.authorZheng, J.
dc.date.accessioned2021-07-28T12:41:56Z
dc.date.available2021-07-28T12:41:56Z
dc.date.issued2021en
dc.identifier.citationDai X, Xu F, Wang S, Mundra PA, Zheng J. PIKE-R2P: Protein–protein interaction network-based knowledge embedding with graph neural network for single-cell RNA to protein prediction. BMC Bioinformatics . 2021 Jun;22(S6).en
dc.identifier.pmid34078261en
dc.identifier.doi10.1186/s12859-021-04022-wen
dc.identifier.urihttp://hdl.handle.net/10541/624256
dc.description.abstractBackground: Recent advances in simultaneous measurement of RNA and protein abundances at single-cell level provide a unique opportunity to predict protein abundance from scRNA-seq data using machine learning models. However, existing machine learning methods have not considered relationship among the proteins sufficiently. Results: We formulate this task in a multi-label prediction framework where multiple proteins are linked to each other at the single-cell level. Then, we propose a novel method for single-cell RNA to protein prediction named PIKE-R2P, which incorporates protein-protein interactions (PPI) and prior knowledge embedding into a graph neural network. Compared with existing methods, PIKE-R2P could significantly improve prediction performance in terms of smaller errors and higher correlations with the gold standard measurements. Conclusion: The superior performance of PIKE-R2P indicates that adding the prior knowledge of PPI to graph neural networks can be a powerful strategy for cross-modality prediction of protein abundances at the single-cell level.en
dc.language.isoenen
dc.relation.urlhttps://dx.doi.org/10.1186/s12859-021-04022-wen
dc.titlePIKE-R2P: Protein-protein interaction network-based knowledge embedding with graph neural network for single-cell RNA to protein predictionen
dc.typeArticleen
dc.contributor.departmentSchool of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China.en
dc.identifier.journalBMC Bioinformaticsen
dc.description.noteen]
refterms.dateFOA2021-07-28T12:53:18Z


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