Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference 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.Article Citation - WoS: 5Citation - Scopus: 4Active Subnetwork Ga: A Two Stage Genetic Algorithm Approach to Active Subnetwork Search(Bentham Science Publ Ltd, 2017-07-11) Ozisik, Ozan; Bakir-Gungor, Burcu; Diri, Banu; Sezerman, Osman UgurBackground: A group of interconnected genes in a protein-protein interaction network that contains most of the disease associated genes is called an active subnetwork. Active subnetwork search is an NP-hard problem. In the last decade, simulated annealing, greedy search, color coding, genetic algorithm, and mathematical programming based methods are proposed for this problem. Method: In this study, we employed a novel genetic algorithm method for active subnetwork search problem. We used active node list chromosome representation, branch swapping crossover operator, multicombination of branches in crossover, mutation on duplicate individuals, pruning, and two stage genetic algorithm approach. The proposed method is tested on simulated datasets and Wellcome Trust Case Control Consortium rheumatoid arthritis genome-wide association study dataset. Our results are compared with the results of a simple genetic algorithm implementation and the results of the simulated annealing method that is proposed by Ideker et al. in their seminal paper. Results and Conclusion: The comparative study demonstrates that our genetic algorithm approach outperforms the simple genetic algorithm implementation in all datasets and simulated annealing in all but one datasets in terms of obtained scores, although our method is slower. Functional enrichment results show that the presented approach can successfully extract high scoring subnetworks in simulated datasets and identify significant rheumatoid arthritis associated subnetworks in the real dataset. This method can be easily used on the datasets of other complex diseases to detect disease-related active subnetworks. Our implementation is freely available at https://www.ce.yildiz.edu.tr/personal/ozanoz/file/6611/ActSubGA.