Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2021
Title: Anti-cancer Drug Activity Prediction by Ensemble Learning
Authors: Tolan, Ertan
Tan, Mehmet
110845
Keywords: Cancer
Drug Activity
Ensemble Learning
Issue Date: 2016
Publisher: SCITEPRESS
Source: Tolan, E., & Tan, M. (2016, November). Anti-cancer Drug Activity Prediction by Ensemble Learning. In Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 431-436). SCITEPRESS-Science and Technology Publications, Lda.
Abstract: Personalized cancer treatment is an ever-evolving approach due to complexity of cancer. As a part of personalized therapy, effectiveness of a drug on a cell line is measured. However, these experiments are backbreaking and money consuming. To surmount these difficulties, computational methods are used with the provided data sets. In the present study, we considered this as a regression problem and designed an ensemble model by combining three different regression models to reduce prediction error for each drug-cell line pair. Two major data sets were used to evaluate our method. Results of this evaluation show that predictions of ensemble method are significantly better than models per se. Furthermore, we report the cytotoxicty predictions of our model for the drug-cell line pairs that do not appear in the original data sets.
Description: 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR) (2016 : Porto; Portugal)
URI: http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006085704310436
https://hdl.handle.net/20.500.11851/2021
ISBN: 978-989-758-203-5
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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