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Joint learning improves protein abundance prediction in cancers

dc.contributor.authorLi, Hongyang
dc.contributor.authorSiddiqui, Omer
dc.contributor.authorZhang, Hongjiu
dc.contributor.authorGuan, Yuanfang
dc.date.accessioned2022-08-10T18:07:43Z
dc.date.available2022-08-10T18:07:43Z
dc.date.issued2019-12-23
dc.identifier.citationBMC Biology. 2019 Dec 23;17(1):107
dc.identifier.urihttps://doi.org/10.1186/s12915-019-0730-9
dc.identifier.urihttps://hdl.handle.net/2027.42/173531en
dc.description.abstractAbstract Background The classic central dogma in biology is the information flow from DNA to mRNA to protein, yet complicated regulatory mechanisms underlying protein translation often lead to weak correlations between mRNA and protein abundances. This is particularly the case in cancer samples and when evaluating the same gene across multiple samples. Results Here, we report a method for predicting proteome from transcriptome, using a training dataset provided by NCI-CPTAC and TCGA, consisting of transcriptome and proteome data from 77 breast and 105 ovarian cancer samples. First, we establish a generic model capturing the correlation between mRNA and protein abundance of a single gene. Second, we build a gene-specific model capturing the interdependencies among multiple genes in a regulatory network. Third, we create a cross-tissue model by joint learning the information of shared regulatory networks and pathways across cancer tissues. Our method ranked first in the NCI-CPTAC DREAM Proteogenomics Challenge, and the predictive performance is close to the accuracy of experimental replicates. Key functional pathways and network modules controlling the proteomic abundance in cancers were revealed, in particular metabolism-related genes. Conclusions We present a method to predict proteome from transcriptome, leveraging data from different cancer tissues to build a trans-tissue model, and suggest how to integrate information from multiple cancers to provide a foundation for further research.
dc.titleJoint learning improves protein abundance prediction in cancers
dc.typeJournal Article
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/173531/1/12915_2019_Article_730.pdf
dc.identifier.doihttps://dx.doi.org/10.7302/5262
dc.language.rfc3066en
dc.rights.holderThe Author(s).
dc.date.updated2022-08-10T18:07:42Z
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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