Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 2Combining Classifiers for Protein Secondary Structure Prediction(Institute of Electrical and Electronics Engineers Inc., 2017-09) Aydin, Zafer; Uzut, Ömmu GülsümConference Object Identify Commonly Affected Pathways in Psychiatric Diseases(Institute of Electrical and Electronics Engineers Inc., 2018-09) Bulut, Umit; Bakir-Güngör, BurcuGenome-wide association studies (GWAS) are an extraordinary source of information when it comes to revealing the common variations of human complex diseases. Until now, the large amount of data generated from these studies have not been shown its full potential enough to identify the molecular and functional framework to be able to understand how a molecular system works. Following a more specific perspective, this study focused on the identification of commonly affected pathways of psychiatric diseases. The pathway term as used in molecular biology, depicts a simplified model of a process within the cell or tissue. Lately, several GWAS datasets are publicly available for various disease types such as psychiatric, immune-related, neurodegenerative, cardiovascular and such. A study on each disease and pairwise comparison to understand the behavior of disease and system would be time consuming and exhaustive. Instead of handling the results of these studies one by one, grouping diseases by target points is a more efficient way. This work aims to get one step closer to reveal key points of diseases and target these points to develop personalized medicine approaches. Especially for complex diseases, every drug doesn't show the same effect in every people. This paper contains the definition of molecular pathways, methods to identify disease related pathways, and to find common pathways pairwise in psychiatric diseases. © 2019 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 3A Computational Drug Repositioning Effort Using Patients' Reviews Dataset(Institute of Electrical and Electronics Engineers Inc., 2023-07-25) Akkaya, Ali; Bakal, GokhanThe drug discovery process is one of the core motivations in both medical and, specifically, pharmaceutical disciplines. Due to the nature of the process, it requires an excessive amount of time, clinical experiments, and budget to cover each discovery phase. In this sense, computational drug discovery efforts can shorten the discovery process by providing plausible candidates since many of the attempts fail for several reasons, such as a lack of participants, financial problems, or ineffective results. In this study, the goal is to identify plausible candidate drugs for diseases. To do that, we utilize a personal experience of drugs dataset generated by patients. Beyond the user-generated comments, the users also give a rate between 1 and 10. Since we want to ensure the dataset quality, we first performed sentiment analysis experiments to prove that the reviews/comments are consistent with the given rating score. Then, only the review pairs having an effectiveness rate of 6 or more are selected as pre-filtered drug-disease pairs. We also build a knowledge graph using treatment-related biomedical relations using predications from Semantic Medline Database to identify drug similarities utilizing the Simrank similarity algorithm. As a result, we reported a list of plausible drugs as repurposing/repositioning candidates for further experiments. © 2023 Elsevier B.V., All rights reserved.Conference Object A Comparative Study on Psychiatric Disorders: Identification of Shared Pathways and Common Agents(Institute of Electrical and Electronics Engineers Inc., 2022-09-07) Kuzudisli, Cihan; Bakir-Güngör, Burcu; Bakir Gungor, BurcuDistinct but closely related diseases generally present shared symptoms, which address possible overlaps among their pathogenic mechanisms. Identification of significantly impacted shared pathways and other common agents are expected to elucidate etiology of these disorders and to help design better intervention strategies. In this research effort, we studied six psychiatric disorders including schizophrenia (SCZ), anorexia (AN), bipolar disorder (BD), depressive disorder (DD), autism (AU) and attention deficit hyperactivity disorder (ADHD). Our methodology can be classified into the following two parts: In Part I, common susceptibility genes; and in Part II, genome-wide association studies (GWAS) data were used to find enriched pathways of psychiatric disorders. 59 KEGG pathways were commonly identified in both parts. 31 of these pathways are disease pathways. Pathways related to cancer and infectious diseases were predominant compared to others. Most of the acquired pathways were in accordance with previous studies in literature. A combination of susceptibility genes and GWAS data is an effective approach to identify significantly impacted pathways in multifactorial diseases. In this respect, shared modules were determined after applying hierarchical clustering of the enriched pathways. These identified modules may tell us the association of psychiatric disorders with the enriched pathways. Taken all together, common pathways and shared modules are expected to highlight the causative factors and important mechanisms behind complex psychiatric diseases, leading to effective drug discovery. © 2022 Elsevier B.V., All rights reserved.