Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Access type: Metadata onlySubject: search.filters.subject.Drug discovery

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  • Book Part
    Citation - Scopus: 3
    Personalized Biomedicine in Cancer: From Traditional Therapy To Sustainable Healthcare
    (Elsevier, 2020) Ulu,G.T.; Kiraz,Y.; Baran,Y.
    What images are coming to your mind when you think about sustainable and qualified life? The main picture drawn is healthcare. Many people suffer from cancer; more than 18.1 million people were diagnosed with cancer and 9.6 million people died from cancer worldwide in 2018. Therefore many diagnosis and treatment strategies that are shaped and regulated by biomedicine approaches have been developed to solve this problem. Biomedicine is an interdisciplinary science to understand the interaction of biological, chemical, and physiological principles. These principles should be brought together to be applicable and sustainable for qualified life. Drug discovery and combination therapy using nanocarriers and natural compounds are being innovated as new approaches and opportunities for cancer treatment. Theoretically and practically, there is no limit to the development of new biomedicinal tools for personalized medicine in cancer. Therefore personalized medicine plays an important role for reaching successful therapy with low cost. By discovering the diverse potential of biomedicine, we can provide better healthcare in the world. © 2020 Elsevier Inc. All rights reserved.
  • Article
    Citation - Scopus: 4
    Quasi-Supervised Strategies for Compound-Protein Interaction Prediction
    (John Wiley and Sons Inc, 2022) Çakı, O.; Karaçalı, B.
    In-silico compound-protein interaction prediction addresses prioritization of drug candidates for experimental biochemical validation because the wet-lab experiments are time-consuming, laborious and costly. Most machine learning methods proposed to that end approach this problem with supervised learning strategies in which known interactions are labeled as positive and the rest are labeled as negative. However, treating all unknown interactions as negative instances may lead to inaccuracies in real practice since some of the unknown interactions are bound to be positive interactions waiting to be identified as such. In this study, we propose to address this problem using the Quasi-Supervised Learning (QSL) algorithm. In this framework, potential interactions are predicted by estimating the overlap between a true positive dataset of compound-protein pairs with known interactions and an unknown dataset of all the remaining compound-protein pairs. The potential interactions are then identified as those in the unknown dataset that overlap with the interacting pairs in the true positive dataset in terms of the associated similarity structure. We also address the class-imbalance problem by modifying the conventional cost function of the QSL algorithm. Experimental results on GPCR and Nuclear Receptor datasets show that the proposed method can identify actual interactions from all possible combinations. © 2021 Wiley-VCH GmbH.