Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Quasi-Supervised Strategies for Compound-Protein Interaction Prediction [master Thesis](01. Izmir Institute of Technology, 2021) Çakı, Onur; Karaçalı, BilgeIn-silico prediction of compound-protein interaction using computational methods preserves its importance in various pharmacology applications 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 algorithm. In this framework, potential interactions are predicted by estimating the overlap between two datasets: a true positive dataset which consists of compound-protein pairs with known interactions and an unknown dataset which consists 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 between interacting pairs. Experimental results on GPCR and Nuclear Receptor datasets show that the proposed method can identify actual interactions from all possible combinations.Master Thesis Bioinformatic Analysis and Biostatistical Modelling of Genetic Interactions Between Microbiota and Host(01. Izmir Institute of Technology, 2020) Musa, Farid; Sezgin, EfeAdvances in genome sequencing technology have revolutionized the study of microorganisms. Recent genome-wide association studies (GWAS) on gut microbiota revealed fascinating discoveries about the effect of microbiota on our health. In this thesis, Drosophila Melanogaster samples were used to investigate the associations between the host's genotype and microbiota. The meta-analysis of microbiota data was performed using PhyloMAF, a novel, and comprehensive microbiome meta-analysis framework. The resulting microbial abundance tables were analyzed using alpha and phylogenetic beta bio-diversity metrics, which were used in the microbiome GWAS study. Significant variant associations were further analyzed in the post-GWAS analysis. The results of our study show that several genomic variants are significantly associated with bio-diversity estimates. Among identified variants, few were found to be associated with more specific phenotypes. Particularly, the gene involved in folate transport and linked to folate malabsorption was found to be associated with Proteobacteria. The latter for its part was found to be one of the primary phyla containing the highest number of genes responsible for de-novo folate synthesis. Similarly, the fly gene related to immune function with the human homologous gene linked to the inflammatory gut disease was found to be associated with the Acetobacter genus. This genus based on the literature survey was found to be associated with an immune deficiency in a fruit fly. In summary, this research revealed captivating findings of genetic factors associated with fruit fly microbiota. The limitations and future directions were stated in order to provide the basis for future prospective studies.