PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Topological Feature Generation for Link Prediction in Biological Networks(PeerJ Inc, 2023-05-09) Temiz, Mustafa; Bakir-Gungor, Burcu; Sahan, Pinar Guner; Coskun, Mustafa; Güner Şahan, PınarGraph or network embedding is a powerful method for extracting missing or potential information from interactions between nodes in biological networks. Graph embedding methods learn representations of nodes and interactions in a graph with low-dimensional vectors, which facilitates research to predict potential interactions in networks. However, most graph embedding methods suffer from high computational costs in the form of high computational complexity of the embedding methods and learning times of the classifier, as well as the high dimensionality of complex biological networks. To address these challenges, in this study, we use the Chopper algorithm as an alternative approach to graph embedding, which accelerates the iterative processes and thus reduces the running time of the iterative algorithms for three different (nervous system, blood, heart) undirected protein-protein interaction (PPI) networks. Due to the high dimensionality of the matrix obtained after the embedding process, the data are transformed into a smaller representation by applying feature regularization techniques. We evaluated the performance of the proposed method by comparing it with state-of-the-art methods. Extensive experiments demonstrate that the proposed approach reduces the learning time of the classifier and performs better in link prediction. We have also shown that the proposed embedding method is faster than state-of-the-art methods on three different PPI datasets.Article Citation - WoS: 6Citation - Scopus: 7Therapeutic Potential of Nitrogen-Substituted Oleanolic Acid Derivatives in Neuroinflammatory and Cytokine Pathways: Insights From Cell-Based and Computational Models(Wiley-VCH Verlag GmbH, 2025-04-22) Turgut, Gurbet Celik; Pepe, Nihan Aktas; Ekiz, Yagmur Ceylan; Senol, Halil; Sen, AlaattinThis study was conducted to investigate the mechanism of the potential and anti-inflammatory properties of nitrogen-substituted oleanolic acid derivatives that can be used to treat neuroinflammatory diseases. Nitrogen-containing oleanolic acid derivatives have been evaluated for their anti-neuroinflammatory effects in vitro in neuronal and monocytic cell lines at nontoxic doses, and the production of cytokines (TNF-alpha, IL-6 and IL-17), the inflammatory enzyme induced nitric oxide synthase (iNOS) and NF-kappa B signalling under LPS-stimulated conditions, and the expression of genes associated with Alzheimer's disease have been assessed. In addition, molecular docking and molecular dynamics simulation assessments are conducted in silico. Key protein markers of neurodegenerative diseases, especially Alzheimer's disease and neuroinflammation, TAU protein levels, and microglial activation, as well as ionised calcium-binding adaptor protein-1 (IBA1) levels, were significantly reduced with the addition of oleanolic acid derivatives. LPS-induced NF-kappa B luciferase reporter activity and iNOS activity were significantly inhibited, approaching the levels in uninduced controls. The mRNA expression of proinflammatory cytokines critical for neuroinflammation, such as TNF-alpha, NF-kappa B, IL-6 and IL-17, was reduced twofold to sevenfold. Furthermore, the molecular docking and MD simulation analyses revealed potential interactions with the TNF-alpha and NF-kappa B proteins. These findings underscore the potential of oleanolic acid derivatives, particularly compound 16, as candidates for further development as therapeutic agents for neurodegenerative diseases associated with chronic inflammation.Article Theoretical Investigation of Steric Effects on the S1 Potential Energy Surface of O-Carborane Derivatives(Tubitak Scientific & Technological Research Council Turkey, 2023-01-01) Alkan, FahriTDDFT scan calculations were performed for s-carborane-anthracene derivatives (o-CB-X-Ant where X=-H,-CH3,-C2H5 and tert-butyl or-tBu) in order to understand the interplay between the steric effects, S1 potential energy surface (PES) and photophysical properties. The results show that all systems exhibit three local minima on the S1 PES, which correspond to the emissive LE and TICT state, along with the nonemissive CT state respectively. In the case of the unsubstituted system (o-CB-H-Ant), and-CH3 and-C2H5 substituted cases, S1 PES is predicted to be quite flat for certain conformations indicating that it is possible for these systems to reach the nonemissive CT state without a large energy penalty. In comparison, conformational pathways for the nonemissive CT state are predicted to be energetically unfavorable for o-CB-tBu-Ant as a result of both steric and electronic effects. These results provide a mechanism for the enhanced emission of cr-CB-fluorophore molecules with bulky ligands.Article Citation - WoS: 1Citation - Scopus: 1The Impact of COVID-19 on Healthcare Utilization in Turkey(Elsevier, 2024-09) Ugur, Zeynep B.; Durak, AysenurObjectives: This study investigates the impact of the COVID-19 pandemic on healthcare utilization in Turkey. Methods: We utilized individual-level data derived from Turkish Statistical Institute 's annual surveys between 2014 and 2022 and estimated probit regression models. Results: We find that COVID-19 pandemic reduced healthcare utilization by 11.8% after taking into account a large set of background variables. Although our study finds that the elderly and those with health problems are more likely to use healthcare services under normal circumstances, the COVID-19 pandemic has caused notable drops in the healthcare utilization among the elderly (-6.5%) and those with health problems (-3.8%). Although those without health insurance had lower utilization of healthcare services before the pandemic, during the pandemic they were not particularly hit. Conclusion: We conclude that the pandemic did not lower the healthcare utilization in Turkey because of the supply constraints. Also, the evidence points to the reduced demand due to the fear of contagion rather than financial concerns.Article Citation - WoS: 9Citation - Scopus: 13The Impact and Future of Artificial Intelligence in Medical Genetics and Molecular Medicine: An Ongoing Revolution(Springer Heidelberg, 2024-08) Ozcelik, Firat; Dundar, Mehmet Sait; Yildirim, A. Baki; Henehan, Gary; Vicente, Oscar; Sanchez-Alcazar, Jose A.; Dundar, MunisArtificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.Article Citation - WoS: 48Citation - Scopus: 65Review of Feature Selection Approaches Based on Grouping of Features(PeerJ Inc, 2023-07-17) Kuzudisli, Cihan; Bakir-Gungor, Burcu; Bulut, Nurten; Qaqish, Bahjat; Yousef, MalikWith the rapid development in technology, large amounts of high-dimensional data have been generated. This high dimensionality including redundancy and irrelevancy poses a great challenge in data analysis and decision making. Feature selection (FS) is an effective way to reduce dimensionality by eliminating redundant and irrelevant data. Most traditional FS approaches score and rank each feature individually; and then perform FS either by eliminating lower ranked features or by retaining highly -ranked features. In this review, we discuss an emerging approach to FS that is based on initially grouping features, then scoring groups of features rather than scoring individual features. Despite the presence of reviews on clustering and FS algorithms, to the best of our knowledge, this is the first review focusing on FS techniques based on grouping. The typical idea behind FS through grouping is to generate groups of similar features with dissimilarity between groups, then select representative features from each cluster. Approaches under supervised, unsupervised, semi supervised and integrative frameworks are explored. The comparison of experimental results indicates the effectiveness of sequential, optimization-based (i.e., fuzzy or evolutionary), hybrid and multi-method approaches. When it comes to biological data, the involvement of external biological sources can improve analysis results. We hope this work's findings can guide effective design of new FS approaches using feature grouping.Article Citation - WoS: 18Citation - Scopus: 20Resveratrol Triggers Anti-Proliferative and Apoptotic Effects in FLT3-LTD Acute Myeloid Leukemia Cells via Inhibiting Ceramide Catabolism Enzymes(Humana Press inc, 2022-01-20) Ersoz, Nur Sebnem; Adan, AysunResveratrol possesses well-defined anti-carcinogenic activities. However, how resveratrol exerts its anti-leukemic actions by modulating anti-apoptotic ceramide catabolism enzymes, mainly sphingosine kinase (SK-1) and glucosylceramide synthase (GCS), in FLT3-ITD AML remains unclear. Resveratrol, SKI II (SK inhibitor) and PDMP (GCS inhibitor) were evaluated alone or in combinations for their effect on cell proliferation (MTT assay), apoptosis (annexin V-FITC/PI staining by flow cytometry) and cell cycle progression (PI staining by flow cytometry) in MOLM-13 and MV4-11 cells. The combination indexes (CIs) were calculated based on cell proliferation data using CompuSyn software. Caspase-3 and PARP activation, changes in SK-1 and GCS levels by resveratrol alone or PARP cleavage in co-treatments were determined by western blot. Resveratrol and inhibitors alone inhibited cell proliferation in a dose- and time-dependent manner. Resveratrol downregulated SK-1 and GCS expression in both cell lines. It induced apoptosis by phosphatidylserine (PS) exposure together with caspase-3 and PARP cleavage and arrested the cell cycle slightly at the S phase. Co-administrations intensified resveratrol's effect by inhibiting cell proliferation synergistically (A CI of < 1) or additively (A CI 1.0-1.1) and inducing apoptosis via PS relocalization and PARP cleavage. Resveratrol plus SKI II did not affect cell cycle progression significantly, however, resveratrol plus PDMP blocked cycle progression at G0/G1 and S phases for MOLM-13 cells and MV4-11 cells, respectively. Overall, resveratrol may inhibit FLT3-ITD AML cell proliferation by inhibiting ceramide catabolism and be evaluated as a chemopreventive after detailed analysis of the crosstalk between resveratrol and ceramide catabolism pathway.Article Citation - WoS: 3Citation - Scopus: 4Rapamycin and Niacin Combination Induces Apoptosis and Cell Cycle Arrest Through Autophagy Activation on Acute Myeloid Leukemia Cells(Springer, 2024-12-23) Subay, Lale Beril; Akcok, Emel Basak Gencer; Akcok, Ismail; Gencer Akçok, Emel BaşakBackgroundAcute myeloid leukemia (AML) is a heterogeneous hematological malignancy caused by disorders in stem cell differentiation and excessive proliferation resulting in clonal expansion of dysfunctional cells called myeloid blasts. The combination of chemotherapeutic agents with natural product-based molecules is promising in the treatment of AML. In this study, we aim to investigate the anti-cancer effect of Rapamycin and Niacin combination on THP-1 and NB4 AML cell lines.Methods and ResultsThe anti-proliferative effects of Rapamycin and Niacin were determined by MTT cell viability assay in a dose- and time-dependent manner. The combination indexes were calculated by isobologram analysis. Furthermore, apoptosis was investigated by Annexin-V/Propidium Iodide(PI) double staining and cell cycle distribution was measured by PI staining. The expression levels of autophagy-related proteins were detected by western blotting. The combination of Rapamycin and Niacin synergistically decreased cell viability of AML cell lines. The combination treatment induced the apoptotic cell population of THP-1 and NB4 by 4.9-fold and 7.3-fold, respectively. In THP-1 cells, the cell cycle was arrested at the G2/M phase by 10% whereas the NB4 cells were accumulated at the G0/G1 phase. The combination treatment decreased Akt and p-Akt expression. Besides, the ATG7 expression was reduced by combination treatment on THP-1 cells. Similarly, the ATG5 level was downregulated in NB4 cells. The level of LC3B-II/LC3B-I, which is an indicator of autophagy flux, was upregulated in THP-1 and NB4 cells.ConclusionAlthough further studies are required, the combination of Rapamycin and Niacin combats cell proliferation by inducing cellular apoptosis, cell cycle arrest and autophagy activation.Article Citation - WoS: 3Citation - Scopus: 4Prediction of Biomechanical Properties of Ex Vivo Human Femoral Cortical Bone Using Raman Spectroscopy and Machine Learning Algorithms(Elsevier, 2025-09) Unal, Mustafa; Unlu, Ramazan; Uppuganti, Sasidhar; Nyman, Jeffry S.This study applied Raman spectroscopy (RS) to ex vivo human cadaveric femoral mid-diaphysis cortical bone specimens (n = 118 donors; age range 21-101 years) to predict fracture toughness properties via machine learning (ML) models. Spectral features, together with demographic variables (age, sex) and structural parameters (cortical porosity, volumetric bone mineral density), were fed into support vector regression (SVR), extreme tree regression (ETR), extreme gradient boosting (XGB), and ensemble models to predict fracture-toughness metrics such as crack-initiation toughness (Kinit) and energy-to-fracture (J-integral). Feature selection was based on Raman-derived mineral and organic matrix parameters, such as nu 1Phosphate (PO4)/CH2-wag, nu 1PO4/ Amide I, and others, to capture the complex composition of bone. Our results indicate that ensemble models consistently outperformed individual models, with the best performance for crack initiation toughness (Kinit) prediction being achieved using the ensemble approach. This yielded a coefficient of determination (R2) of 0.623, root-mean squared error (RMSE) of 1.320, mean absolute error (MAE) of 1.015, and mean percentage absolute error (MAPE) of 0.134. For prediction of the overall energy to propagate a crack (J-integral), the XGB model achieved an R2 of 0.737, RMSE of 2.634, MAE of 2.283, and MAPE of 0.240. This study highlights the importance of incorporating mineral quality properties (MP) and organic matrix properties (OMP) for enhanced prediction accuracy. This work represents the first-ever study combining Raman spectroscopy with other clinical and structural features to predict fracture toughness of human cortical bone, demonstrating the potential of artificial intelligence (AI) and ML in advancing bone research. Future studies could focus on larger datasets and more advanced modeling techniques to further improve predictive capabilities.Article Citation - WoS: 2Citation - Scopus: 2Possible Boron-Rich Amorphous Silicon Borides From Ab Initio Simulations(Springer, 2023-03-10) Karacaoglan, Aysegul Ozlem Cetin; Durandurdu, MuratContextBy means of ab initio molecular dynamics simulations, possible boron-rich amorphous silicon borides (BnSi1-n, 0.5 <= n <= 0.95) are generated and their microstructure, electrical properties and mechanical characters are scrutinized in details. As expected, the mean coordination number of each species increases progressively and more closed packed structures form with increasing B concentration. In all amorphous models, pentagonal pyramid-like configurations are observed and some of which lead to the development of B-12 and B11Si icosahedrons. It should be noted that the B11Si icosahedron does not form in any crystalline silicon borides. Due to the affinity of B atoms to form cage-like clusters, phase separations (Si:B) are perceived in the most models. All simulated amorphous configurations are a semiconducting material on the basis of GGA+U calculations. The bulk modulus of the computer-generated amorphous compounds is in the range of 90 GPa to 182 GPa. As predictable, the Vickers hardness increases with increasing B content and reaches values of 25-33 GPa at 95% B concentration. Due to their electrical and mechanical properties, these materials might offer some practical applications in semiconductor technologies.MethodThe density functional theory (DFT) based ab initio molecular dynamics (AIMD) simulations were used to generate B-rich amorphous configurations.