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dc.contributor.authorOzgul, O.F.
dc.contributor.authorBardak, Batuhan
dc.contributor.authorTan, Mehmet
dc.date.accessioned2019-07-10T14:42:46Z
dc.date.available2019-07-10T14:42:46Z
dc.date.issued2018-07-05
dc.identifier.citationÖzgül, O. F., Bardak, B., and 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.urihttps://ieeexplore.ieee.org/document/8404799
dc.identifier.urihttp://hdl.handle.net/20.500.11851/2026
dc.description26th IEEE Signal Processing and Communications Applications Conference (2018 : Izmir; Turkey)
dc.description.abstractDeveloping 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.sponsorshipAselsan,et al.,Huawei,IEEE Signal Processing Society,IEEE Turkey Section,Netas
dc.language.isoturen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectNeoplasmsen_US
dc.subjectPharmaceutical Preparationsen_US
dc.subjectsensitivity predictionen_US
dc.titlePredicting drug activity by image encoded gene expression profilesen_US
dc.title.alternativeGen ifade profillerinin görüntü ile temsili ve ilaç aktivite tahminitr_TR
dc.typeconferenceObjecten_US
dc.contributor.departmentTOBB ETU, Faculty of Engineering, Department of Computer Engineeringen_US
dc.contributor.departmentTOBB ETÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage1
dc.identifier.endpage4
dc.contributor.orcidhttps://orcid.org/0000-0002-1741-0570
dc.identifier.scopus2-s2.0-85050790428
dc.contributor.tobbetuauthorTan, Mehmet
dc.identifier.doi10.1109/SIU.2018.8404799
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıtr_TR


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