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dc.contributor.authorRossi, Emanueleen
dc.contributor.authorMonti, Federicoen
dc.contributor.authorBronstein, Michaelen
dc.contributor.authorLio, Pietroen
dc.date.accessioned2019-07-25T15:29:49Z
dc.date.available2019-07-25T15:29:49Z
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/294903
dc.description.abstractNon-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions. The task of ncRNA classification consists in classifying a given ncRNA sequence into its family. While it has been shown that the graph structure of an ncRNA sequence folding is of great importance for the prediction of its family, current methods make use of machine learning classifiers on hand-crafted graph features. We improve on the state-of-the-art for this task with a graph convolutional network model which achieves an accuracy of 85.73% and an F1-score of 85.61% over 13 classes. Moreover, our model learns in an end-to-end fashion from the raw RNA graphs and removes the need for expensive feature extraction. To the best of our knowledge, this also represents the first successful application of graph convolutional networks to RNA folding data.
dc.publisherAssociation for Computing Machinery
dc.rightsAll rights reserved
dc.rights.uri
dc.subjectq-bio.GNen
dc.subjectq-bio.GNen
dc.subjectcs.LGen
dc.subjectstat.MLen
dc.titlencRNA Classification with Graph Convolutional Networksen
dc.typeConference Object
dc.identifier.doi10.17863/CAM.41991
dcterms.dateAccepted2019-06-05en
rioxxterms.versionAM*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-06-05en
dc.contributor.orcidLio, Pietro [0000-0002-0540-5053]
rioxxterms.typeConference Paper/Proceeding/Abstracten
rioxxterms.freetoread.startdate2019-08-06


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