Browsing by Author "Özyer, Tansel"

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    Computational Drug Repositioning Based on Integrated Similarity Measures and Deep Learning
    (2020-09-11) Jarada, Tamer N R; Rokne, Jon G.; Alhajj, Reda S.; Özyer, Tansel; Helaoui, Mohamed; Sadaoui, Samira
    Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to the traditional \textit{de novo} drug discovery process. Advances in the genomics, together with the enormous growth of large-scale publicly available data and the availability of high-performance computing capabilities, have further motivated the development of computational drug repositioning approaches. Numerous attempts have been carried out, with different degrees of efficiency and success, to computationally study the potential of identifying alternative drug indications, which slow, stop, or reverse the courses of incurable diseases. More recently, the rise of machine learning techniques, together with the availability of powerful computers, has made the area of computational drug repositioning an area of intense activities. In this thesis, the integration of various biological and biomedical data from different sources to improve the quality of biomedical knowledge in the computational drug repositioning field is addressed. The main contribution of this thesis is four-fold. First, it provides a comprehensive review of drug repositioning strategies, resources, and computational approaches. Second, it develops an approach for identifying disease-specific gene associations, which can be further used as a resource for computational drug repositioning methods. Third, it proposes a robust framework that utilizes known drug-disease interactions and drug-related similarity information to predict new drug-disease interactions. Fourth, it introduces a novel integrative framework for predicting drug-disease interactions using known drug-disease interactions, drug-related similarity information, and disease-related similarity information. The two proposed frameworks leverage advanced similarity calculation, selection, and integration to understand the functional and behavioural correlation between drugs and diseases. Furthermore, they employ the most advanced machine learning tools in predicting hidden or indirect drug-disease interactions for potential drug repositioning applications.
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    Correction to: Realizing drug repositioning by adapting a recommendation system to handle the process
    (2018-07-02) Ozsoy, Makbule G; Özyer, Tansel; Polat, Faruk; Alhajj, Reda
    Following publication of the original article [1], the authors reported that there was an error in the spelling of the name of one of the authors.
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    ItemOpen Access
    Realizing drug repositioning by adapting a recommendation system to handle the process
    (2018-04-12) Ozsoy, Makbule G; Özyer, Tansel; Polat, Faruk; Alhajj, Reda
    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.