Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/757
Title: Realizing drug repositioning by adapting a recommendation system to handle the process
Authors: Özsoy, Makbule Gülçin
Özyer, Tansel
Polat, Faruk
Alhajj, Reda
143116
Keywords: Drug repositioning
Multiple data sources
Multiple features
Pareto dominance
Collaborative filtering
Recommendation systems
Issue Date: 12-Apr-2018
Publisher: BioMed Central Ltd.
Source: Ozsoy, M. G., Özyer, T., Polat, F., & Alhajj, R. (2018). Realizing drug repositioning by adapting a recommendation system to handle the process. BMC bioinformatics, 19(1), 136.
Abstract: Background: Drug repositioning is the process of identifying new targets for known drugs. It can be used to overcome problems associated with traditional drug discovery by adapting existing drugs to treat new discovered diseases. Thus, it may reduce associated risk, cost and time required to identify and verify new drugs. Nowadays, drug repositioning has received more attention from industry and academia. To tackle this problem, researchers have applied many different computational methods and have used various features of drugs and diseases. Results: In this study, we contribute to the ongoing research efforts by combining multiple features, namely chemical structures, protein interactions and side-effects to predict new indications of target drugs. To achieve our target, we realize drug repositioning as a recommendation process and this leads to a new perspective in tackling the problem. The utilized recommendation method is based on Pareto dominance and collaborative filtering. It can also integrate multiple data-sources and multiple features. For the computation part, we applied several settings and we compared their performance. Evaluation results show that the proposed method can achieve more concentrated predictions with high precision, where nearly half of the predictions are true. Conclusions: Compared to other state of the art methods described in the literature, the proposed method is better at making right predictions by having higher precision. The reported results demonstrate the applicability and effectiveness of recommendation methods for drug repositioning.
URI: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2142-1
https://hdl.handle.net/20.500.11851/757
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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