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ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles

dc.contributor.authorAnene, CA
dc.contributor.authorKhan, F
dc.contributor.authorBewicke-Copley, F
dc.contributor.authorManiati, E
dc.contributor.authorWang, J
dc.date.accessioned2021-06-14T11:26:16Z
dc.date.available2021-06-14T11:26:16Z
dc.date.issued2021-06
dc.identifier.citationChinedu Anthony Anene, Faraz Khan, Findlay Bewicke-Copley, Eleni Maniati, Jun Wang, ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles, Patterns, Volume 2, Issue 6, 2021, 100270, https://doi.org/10.1016/j.patter.2021.100270.en_US
dc.identifier.issn2666-3899
dc.identifier.other100270
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/72501
dc.format.extent100270 - 100270
dc.languageen
dc.publisherElsevier BVen_US
dc.relation.ispartofPatterns
dc.rightsThis is an open access article under the CC BY license
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profilesen_US
dc.typeArticleen_US
dc.rights.holder(c) 2021 The Author(s).
dc.identifier.doi10.1016/j.patter.2021.100270
pubs.issue6en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume2en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US


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