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Predicting drug activity by image encoded gene expression profiles

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dc.contributor.author Ozgul, O.F.
dc.contributor.author Bardak, B.
dc.contributor.author Tan, Mehmet
dc.date.accessioned 2019-07-10T14:42:46Z
dc.date.available 2019-07-10T14:42:46Z
dc.date.issued 2018-07-05
dc.identifier.citation Özgül, O. F., Bardak, B., & Tan, M. (2018, May). Predicting drug activity by image encoded gene expression profiles. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. en_US
dc.identifier.uri https://ieeexplore.ieee.org/document/8404799
dc.identifier.uri http://hdl.handle.net/20.500.11851/2026
dc.description 26th IEEE Signal Processing and Communications Applications Conference (2018 : Izmir; Turkey)
dc.description.abstract Developing personalized cancer treatment procedures requires a prior knowledge on the effects of different drugs on cancer cell lines. While obtaining this information in vitro is a tedious task, the emergence of numerous large-scale datasets facilitates the usage of machine learning algorithms for this purpose. Conventional methods make an effort to reveal the mapping function between a cell line's identifying features called gene expressions and a certain drug's effect on it. In this work, we move away from this philosophy and represent cell lines as images in which inter-feature relations are preserved. Once these images are obtained, the regression problem is solved with the help of a convolutional neural network, a neural network architecture proven to work well with image inputs. A benchmarking with the other models in the literature exhibits the fruitfulness of our novel strategy. © 2018 IEEE. en_US
dc.description.sponsorship Aselsan,et al.,Huawei,IEEE Signal Processing Society,IEEE Turkey Section,Netas
dc.language.iso tur en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
dc.rights info:eu-repo/semantics/closedAccess
dc.subject Neoplasms en_US
dc.subject Pharmaceutical Preparations en_US
dc.subject sensitivity prediction en_US
dc.title Predicting drug activity by image encoded gene expression profiles en_US
dc.title.alternative Gen ifade profillerinin görüntü ile temsili ve ilaç aktivite tahmini tr_TR
dc.type conferenceObject en_US
dc.contributor.department TOBB ETU, Faculty of Engineering, Department of Computer Engineering en_US
dc.contributor.department TOBB ETÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR
dc.identifier.startpage 1
dc.identifier.endpage 4
dc.contributor.orcid https://orcid.org/0000-0002-1741-0570
dc.identifier.scopus 2-s2.0-85050790428
dc.contributor.YOKid 110845
dc.identifier.doi 10.1109/SIU.2018.8404799
dc.contributor.wosresearcherID I-2328-2019
dc.contributor.ScopusAuthorID 36984623900
dc.relation.publicationcategory Uluslararası yayın tr_TR


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