Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Book Part Citation - Scopus: 3Personalized 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: 4Quasi-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.Article Citation - WoS: 3Citation - Scopus: 4Quasi-Supervised Strategies for Compound-Protein Interaction Prediction [article](Wiley-VCH Verlag, 2021) Çakı, Onur; Karaçalı, BilgeIn-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.Article Citation - WoS: 10Citation - Scopus: 11A New Drug Testing Platform Based on 3d Tri-Culture in Lab-On Devices(Elsevier, 2020) Gökçe, Begüm; Akçok, İsmail; Çağır, Ali; Pesen Okvur, DevrimDrug discovery has a 90% rate of failure because preclinical platforms for drug testing do not mimic the in vivo conditions. Doxorubicin (DOX) is a commonly used drug to treat breast cancer patients even though it has side effects. Lab-on-a-chip (LOC) devices provide spatial control at the micrometer scale and can thus emulate the cancer microenvironment. Here, using a multidisciplinary approach, a new drug testing platform based on 3D tri-culture in LOC devices was developed. Breast cancer cells alone or with normal mammary epithelial cells and macrophages were cultured in matrigel in LOC devices. The platform was used to test DOX and (R)-4'-methylklavuzon (KLA), which is a new anti-cancer drug candidate. Results showed that DOX and KLA were equally effective on breast cancer cells in 3D monoculture. KLA produced 26% less death for breast cancer cells than DOX in 3D tri-culture. More importantly, DOX was not selective between breast cancer cells and normal mammary epithelial cells in 3D tri- culture whereas KLA caused 56% less cell death than DOX for normal mammary epithelial cells. Results strongly recommend that 3D tri-culture in LOC devices be used for assessment of drug toxicity at the preclinical stage.Article Citation - WoS: 28Citation - Scopus: 45Biodiversity, Drug Discovery, and the Future of Global Health: Introducing the Biodiversity To Biomedicine Consortium, a Call To Action(Edinburgh University Global Health Society, 2017) Neergheen-Bhujun, Vidushi; Awan, Almas Taj; Baran, Yusuf; Bunnefeld, Nils; Chan, Kit; Dela Cruz, Thomas Edison; Egamberdieva, Dilfuza; Elsasser, Simon; Johnson, Mari-Vaughn V.; Komai, Shoji; Konevega, Andrey L.; Malone, John H.; Mason, Paul; Nguon, Rothsophal; Piper, Ross; Shrestha, Uttam Babu; Pesic, Milica; Kagansky, AlexanderLooking to nature for medicine is nothing new – we have been doing it for tens of thousands of years and although modern pharmaceutical science has come a long way from those ancient roots, nature is and will always be an important source of useful compounds and inspiration. Dismissing nature in this regard is a huge mistake as evolution is the greatest problem solver and the myriad compounds produced by the immense variety of species we share the planet with have been honed by three billion years of trial and error. However, with every bit of habitat that disappears under the plough or concrete we impoverish nature and deprive ourselves of potential medicines.