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Machine learning in the prediction of cancer therapy

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dc.contributor.authorRafique, Raihan
dc.contributor.authorIslam, S. M.Riazul
dc.contributor.authorKazi, Julhash U.
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.date.accessioned2023-10-05T15:30:00Z
dc.date.available2023-10-05T15:30:00Z
dc.date.issued2021-07-08
dc.descriptionFunding Information: This research was supported by the Crafoord Foundation (JUK), the Swedish Cancer Society (JUK), and the Swedish Childhood Cancer Foundation (JUK). Open Access funding is provided by Lund University.en
dc.description.statusPeer revieweden
dc.format.extent15
dc.format.extent163226
dc.identifier281140094
dc.identifier396d3773-d56f-4206-a59b-d1ddc86f1d2a
dc.identifier85110763754
dc.identifier.citationRafique, R, Islam, S M R & Kazi, J U 2021, 'Machine learning in the prediction of cancer therapy', Computational and Structural Biotechnology Journal, vol. 19, pp. 4003-4017. https://doi.org/10.1016/j.csbj.2021.07.003en
dc.identifier.doi10.1016/j.csbj.2021.07.003
dc.identifier.issn2001-0370
dc.identifier.otherORCID: /0000-0003-2968-9561/work/144004112
dc.identifier.urihttps://hdl.handle.net/2164/21854
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85110763754&partnerID=8YFLogxKen
dc.identifier.vol19en
dc.language.isoeng
dc.relation.ispartofComputational and Structural Biotechnology Journalen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subjectArtificial intelligenceen
dc.subjectConvolutional neural networken
dc.subjectDeep learningen
dc.subjectDeep neural networken
dc.subjectDrug combinationsen
dc.subjectDrug synergyen
dc.subjectElastic neten
dc.subjectFactorization machineen
dc.subjectGraph convolutional networken
dc.subjectHigher-order factorization machinesen
dc.subjectLassoen
dc.subjectMatrix factorizationen
dc.subjectMonotherapy predictionen
dc.subjectOrdinary differential equationen
dc.subjectRandom forestsen
dc.subjectRestricted Boltzmann machineen
dc.subjectRidge regressionen
dc.subjectSupport vector machinesen
dc.subjectVariational autoencoderen
dc.subjectVisible neural networken
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectBiotechnologyen
dc.subjectBiophysicsen
dc.subjectStructural Biologyen
dc.subjectBiochemistryen
dc.subjectGeneticsen
dc.subjectComputer Science Applicationsen
dc.subject.lccQA75en
dc.titleMachine learning in the prediction of cancer therapyen
dc.typeJournal itemen

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