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dc.contributor.authorGabor, Attila
dc.contributor.authorTognetti, Marco
dc.contributor.authorDriessen, Alice
dc.contributor.authorTanevski, Jovan
dc.contributor.authorGuo, Baosen
dc.contributor.authorCao, Wencai
dc.contributor.authorShen, He
dc.contributor.authorYu, Thomas
dc.contributor.authorChung, Verena
dc.contributor.authorSingle Cell Signaling in Breast Cancer DREAM Consortium members
dc.contributor.authorBodenmiller, Bernd
dc.contributor.authorSaez-Rodriguez, Julio
dc.date.accessioned2021-11-20T00:30:11Z
dc.date.available2021-11-20T00:30:11Z
dc.date.issued2021-10
dc.identifier.issn1744-4292
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330761
dc.description.abstractRecent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time-course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.
dc.languageeng
dc.publisherEuropean Molecular Biology Organization
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectcell signaling
dc.subjectcrowdsourcing
dc.subjectmass cytometry
dc.subjectpredictive modeling
dc.subjectsingle cell
dc.titleCell-to-cell and type-to-type heterogeneity of signaling networks: insights from the crowd.
dc.typeArticle
prism.endingPagee10402
prism.issueIdentifier10
prism.publicationDate2021
prism.publicationNameMolecular Systems Biology
prism.startingPagee10402
prism.volume17
dc.identifier.doi10.17863/CAM.78202
dcterms.dateAccepted2021-09-28
rioxxterms.versionofrecord10.15252/msb.202110402
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021-10
dc.identifier.eissn1744-4292
rioxxterms.typeJournal Article/Review
cam.issuedOnline2021-10-18


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International