PubMed İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397

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  • Article
    Burg-Aided 2D MIMO Array Extrapolation for Improved Spatial Resolution
    (MDPI, 2025-10-12) Bekar, Muge; Bekar, Ali; Pirkani, Anum; Baker, Christopher John; Gashinova, Marina
    In this paper, the extrapolation of a 2D multiple-input multiple-output (MIMO) array is proposed using the Burg algorithm to achieve higher angular resolution beyond that of the corresponding 2D MIMO virtual array. The main advantage of such an approach is that it allows us to dramatically decrease both the physical size and the number of antenna elements of the MIMO array. The performance and limitations of the Burg algorithm are examined through both simulation and experimentation at 77 GHz. The experimental methodology used to acquire 3D data of range, azimuth and elevation information with the 1D MIMO off-the-shelf radar is described. Using this method, the performance of the proposed array can be tested experimentally, especially at frequencies where it is desired to assess the antenna response prior to fabricating the antenna.
  • Correction
    Correction: Engineering Novel Features for Diabetes Complication Prediction Using Synthetic Electronic Health Records
    (Frontiers Media S.A., 2025-08-29) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, Malik
  • Article
    Citation - WoS: 26
    Citation - Scopus: 31
    miRcorrNet: 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
    Citation - WoS: 1
    Citation - Scopus: 1
    Synthesis, Characterization, and Comprehensive in Vitro and in Silico Evaluation of the Anti-Inflammatory Potential of Novel 1,2,3-Triazole–Arylidenehydrazide/Thiazolidinone Hybrids
    (Wiley-VCH verlag GmbH, 2025-09) Pepe, Nihan Aktas; Cakir, Furkan; Atalay, Tugba; Acar, Busra; Turgut, Gurbet Celik; Sen, Alaattin; Senol, Halil
    Five novel 1,2,3-triazole/arylidenehydrazide/thiazolidinone hybrid compounds (7-11) were synthesized and characterized using NMR, HRMS, IR, and HPLC purity analysis. The cytotoxicity of these compounds was evaluated on fibroblasts and THP-1 cells, showing that all compounds were nontoxic at the tested concentrations. The wound healing assay revealed that compounds 7, 9, and 10 significantly enhanced wound closure, with a 7.74%-32.69% improvement in treated cells. Compounds 8 and 11 showed moderate effects. Anti-inflammatory activity was assessed through qRT-PCR, demonstrating that compound 10 led to the most significant reduction in proinflammatory cytokines TNF-alpha, IL-1 beta, and NF-kappa B1. In addition, the expression of Iba1 protein in THP-1 cells confirmed that compound 8 showed the strongest anti-inflammatory effect, surpassing that of aspirin. Compound 10 showed the highest inhibition of NF-kappa B signaling and iNOS activity. Molecular docking studies revealed that compounds 10 and 11 had strong binding affinities to TNF-alpha and iNOS, with compound 11 showing the most stable interactions. Molecular dynamics simulations supported these findings, indicating that compound 11 demonstrated more stable binding to both targets. Overall, the results suggest that compounds 10 and 11 are promising anti-inflammatory candidates with potential for further development in therapeutic applications for inflammatory diseases.