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|Title:||Predicting drug activity by image encoded gene expression profiles||Other Titles:||Gen ifade profillerinin görüntü ile temsili ve ilaç aktivite tahmini||Authors:||Özgül, Ozan Fırat
|Issue Date:||5-Jul-2018||Publisher:||Institute of Electrical and Electronics Engineers Inc.||Source:||Özgül, 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.||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.||Description:||26th IEEE Signal Processing and Communications Applications Conference (2018 : Izmir; Turkey)||URI:||https://ieeexplore.ieee.org/document/8404799
|Appears in Collections:||Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering|
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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checked on Feb 6, 2023
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