TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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Article Citation - WoS: 1Citation - Scopus: 1Enlightening the Molecular Mechanisms of Type 2 Diabetes With a Novel Pathway Clustering and Pathway Subnetwork Approach(Tubitak Scientific & Technological Research Council Turkey, 2022-01-01) Bakir-Gungor, Burcu; Yazici, Miray Unlu; Goy, Gokhan; Temiz, Mustafa; Ünlü Yazici, MirayType 2 diabetes mellitus (T2D) constitutes 90% of the diabetes cases, and it is a complex multifactorial disease. In the last decade, genome-wide association studies (GWASs) for T2D successfully pinpointed the genetic variants (typically single nucleotide polymorphisms, SNPs) that associate with disease risk. In order to diminish the burden of multiple testing in GWAS, researchers attempted to evaluate the collective effects of interesting variants. In this regard, pathway-based analyses of GWAS became popular to discover novel multigenic functional associations. Still, to reveal the unaccounted 85 to 90% of T2D variation, which lies hidden in GWAS datasets, new post-GWAS strategies need to be developed. In this respect, here we reanalyze three metaanalysis data of GWAS in T2D, using the methodology that we have developed to identify disease-associated pathways by combining nominally significant evidence of genetic association with the known biochemical pathways, protein-protein interaction (PPI) networks, and the functional information of selected SNPs. In this research effort, to enlighten the molecular mechanisms underlying T2D development and progress, we integrated different in silico approaches that proceed in top-down manner and bottom-up manner, and presented a comprehensive analysis at protein subnetwork, pathway, and pathway subnetwork levels. Using the mutual information based on the shared genes, the identified protein subnetworks and the affected pathways of each dataset were compared. While most of the identified pathways recapitulate the pathophysiology of T2D, our results show that incorporating SNP functional properties, PPI networks into GWAS can dissect leading molecular pathways, and it could offer improvement over traditional enrichment strategies.Article Citation - WoS: 5Citation - Scopus: 10A New Tool for QT Interval Analysis During Sleep in Healthy and Obstructive Sleep Apnea Subjects: A Study on Women(Tubitak Scientific & Technological Research Council Turkey, 2013) Kaya, Kemal Alican; Yilmaz, BulentBy monitoring the Q wave/T wave (QT) interval computed from electrocardiography (ECG) signals during sleep, it is possible to create a link between the ventricular repolarization and sleep stages. In this study, we aimed to find a robust and simple approach to automatically determine the fiducials on each 30-s sleep epoch, such as the Q, R, and T-end points, on long sleep ECG recordings in order to statistically analyze the effect of obstructive sleep apnea (OSA) and sleep stages on QT intervals. This is a retrospective study in which the ECG data extracted from the polysomnography recordings of 7 healthy women and 5 women with OSA, acquired in a sleep laboratory, were used. Experts annotated the sleep stage and OSA presence information for each 30-s epoch. Later, we visually selected epochs with clean signals from a total of 8324 epochs. On the selected epochs, we determined R peaks on each heartbeat, and by aligning each ECG portion corresponding to a heartbeat using those R points, we computed an average ECG signal for each epoch. On the average ECG signals, we developed a novel approach to find the Q and T-end points. With the help of Bazzet's formula, we computed the corrected QT interval (QTc) values for each epoch using the QT and the median RR interval. Finally, we analyzed the QTc values for the different sleep stages and healthy or OSA groups. We employed statistical approaches such as the Mann-Whitney U test, Freidman's test, and the Wilcoxon signed-rank test. As a result of this study, we found that OSA has a prolongation effect on the total duration of the ventricular depolarization and repolarization. We also observed that the QTc values computed in each sleep stage were significantly different between the healthy and OSA groups. Additionally, we discovered that within the healthy group, the QTc values were distinctive in the different sleep stages.