PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Citation - WoS: 26Citation - Scopus: 31miRcorrNet: Machine Learning-Based Integration of miRNA and mRNA Expression Profiles, Combined with Feature Grouping and Ranking(PeerJ Inc., 2021-05-19) Yousef, M.; Göy, G.; Mitra, R.; Eischen, C.M.; Jabeer, A.; Bakir-Güngör, B.A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/ malikyousef/miRcorrNet. © 2021 Elsevier B.V., All rights reserved.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 Thermal Stresses in SOFC Stacks: The Role of Mismatch Among Thermal Conductivity of Adjacent Components(Tubitak Scientific & Technological Research Council Turkey, 2021-06-30) Aydin, Ozgur; Matsumoto, Go; Shiratori, YusukeGenerating power from renewable biogas in solid oxide fuel cells (SOFCs) is an environment-friendly, efficient, and promising energy conversion process. Biogas can be used in SOFCs via a reforming process for which dry reforming is more suitable as the reforming agent exists in the biogas mixture. Biogas can be directly reformed to H-2 -rich fuel stream in the anode chamber of a SOFC by the heat released during power generation. Exploiting the heat and water produced in the SOFC for internal reforming of biogas makes the energy conversion process very efficient; however, various challenges are reported. Thus, indirect internal reforming is opted for which a separate reforming domain is required. In an indirect internal reformer operating at usual conditions, dry reforming rate is quite high in the inlet and it decreases steeply toward the fuel outlet. Great temperature gradients develop over the reformer, since the dry reforming reaction is strongly endothermic. The abruptly varying rate of the reforming reaction affects the temperature fields in the adjacent components of SOFC and hence intolerable thermal stresses emerge on the SOFC components. In our preceding study, we graded the reforming domain, homogenized the temperature profile over the reforming domain, and executed performance and durability experiments. However, most of the experiments failed due to fracturing SOFC components hinting at existence of thermal stresses. In that study, we focused on minimizing the temperature gradients within the reforming domain; namely, we neglected the other processes. To eliminate the thermal stresses, we modeled the entire module of SOFC equipped with a reformer featuring a graded reforming domain. We found that the mismatch between the thermal conductivities of the adjacent module components is the major reason for the thermal stresses. When the mismatch is eliminated, thermal stresses disappear even if the reforming domain is not graded.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: 3Citation - Scopus: 3Theoretical Investigation of Substituent Effects on the Relative Stabilities and Electronic Structure of [BnXn]2- Clusters(Springer, 2021-11-29) Tahaoglu, Duygu; Alkan, Fahri; Durandurdu, MuratIn this study, we provide a theoretical evaluation of relative stabilities and electronic structure for [BnXn](2-) clusters (n = 10, 12, 13, 14, 15, 16). Structural and electronic characteristics of [BnXn](2-) clusters are examined by comparison with the [B12X12](2-) counterparts with a focus on the substituent effects (X = H, F, Cl, Br, CN, BO, OH, NH2) on the electronic structure, electron detachment energies, formation enthalpies, and charge distributions. For the electronic structure and electron detachment energies, substituent effects on boron clusters are shown to follow a very similar trend to the mesomeric and inductive effects (+/- M and +/- I) of pi-conjugated systems, and the most stable derivatives in terms of HOMO/LUMO and electron detachment energies are calculated for CN and BO substituents due to strong -M effects. In the case of formation enthalpies for larger boron clusters (n >= 13), the icosahedral barrier is shown to increase with the halogen and CN substitution, whereas it is possible to reduce the icosahedral barrier for the cases of X = OH and NH2. It is shown that this reduction results from destabilizing the [B12X12](2-) cluster with electronic (+ M) and symmetry effects induced by OH and NH2 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.