PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Developing a Label Propagation Approach for Cancer Subtype Classification Problem(Tubitak Scientific & Technological Research Council Turkey, 2022-01-01) Guner, Pinar; Bakir-Gungor, Burcu; Coskun, MustafaCancer is a disease in which abnormal cells grow uncontrollably and invade other tissues. Several types of cancer have various subtypes with different clinical and biological implications. Based on these differences, treatment methods need to be customized. The identification of distinct cancer subtypes is an important problem in bioinformatics, since it can guide future precision medicine applications. In order to design targeted treatments, bioinformatics methods attempt to discover common molecular pathology of different cancer subtypes. Along this line, several computational methods have been proposed to discover cancer subtypes or to stratify cancer into informative subtypes. However, existing works do not consider the sparseness of data (genes having low degrees) and result in an ill-conditioned solution. To address this shortcoming, in this paper, we propose an alternative unsupervised method to stratify cancer patients into subtypes using applied numerical algebra techniques. More specifically, we applied a label propagation based approach to stratify somatic mutation profiles of colon, head and neck, uterine, bladder, and breast tumors. We evaluated the performance of our method by comparing it to the baseline methods. Extensive experiments demonstrate that our approach highly renders tumor classification tasks by largely outperforming the state-of-the-art unsupervised and supervised approaches.Article Citation - WoS: 2Citation - Scopus: 2Effect of Yttrium/Lanthanum-Doped Ultrasonically Assisted Nano-Hydroxyapatite on Remineralization and Bracket Bond Strength in Artificial Enamel Lesions(BMC, 2025-09-29) Ozturk, Taner; Mammadov, Elshan; Bulduk Karakaya, Humeyra; Yagci, Filiz; Dayan, Serkan; Yagci, AhmetBackground This in vitro study aimed to evaluate the remineralization efficacy of ultrasonically assisted yttrium fluoride-doped (Ult-YF3-nHAP) and lanthanum fluoride-doped (Ult-LaF3-nHAP) nano-hydroxyapatite (nHAP) on artificially induced enamel lesions (aWSLs), and to compare their performance with acidulated phosphate fluoride (APF) gel, fluoride varnish, casein phosphopeptide-amorphous calcium phosphate (CPP-ACP), and resin infiltrant (ICON). Methods This in vitro study followed a four-phase design: enamel lesion creation, application of remineralization agents, a 14-day treatment protocol, and post-treatment analyses using QLF, Micro-CT, SEM-EDX, and SBS testing. This study included 168 extracted human premolars, divided into eight experimental groups (n = 21 per group): (1) Demineralized control (no remineralization treatment), (2) Acidulated phosphate fluoride (APF) gel, (3) Fluoride varnish, (4) Casein phosphopeptide-amorphous calcium phosphate (CPP-ACP), (5) Ultrasonically assisted nHAP (Control nHAP), (6) Ult-YF3-nHAP, (7) Ult-LaF3-nHAP, and (8) Resin infiltrant (ICON). The aWSLs were created under laboratory conditions. Brackets were bonded to the teeth with composite material, and aWSLs were created under laboratory conditions. After lesion formation and at the end of the experimental process, micro-computed tomography (Micro-CT) and laser-assisted quantitative light fluorescence (QLF) analysis were performed to assess lesion progression and remineralization. Additionally, scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX) and shear bond strength (SBS) tests were conducted at the end of the study. Statistical analysis was performed using one-way ANOVA, Kruskal-Wallis, and Mann-Whitney U tests, with a significance level of p < 0.05. Results The bracket bond strength test data showed no significant differences between the groups (p = 0.156). Significant differences were found among groups for QLF fluorescence recovery (Delta F, p < 0.001), with the Ult-YF3-nHAP group showing the greatest increase (median: +0.5, IQR: -1.4 to + 0.7), while the control group showed the greatest decrease (median: -12.1, IQR: -12.4 to -10.2). Micro-CT analysis also revealed significant differences between groups (p = 0.008). The APF Gel group showed values comparable to those of all other experimental groups. The highest remineralization values were recorded in the Ult-YF3-nHAP group (6.87 +/- 3.03 mm(3)), whereas the lowest values were found in the Varnish group. The demineralized control group had significantly higher values than the Varnish group, but lower than the Ult-LaF3-nHAP group. SEM-EDX analysis revealed that fluoride weight was significantly lower in the Tooth Mousse and Varnish groups compared to the other experimental groups (p < 0.001). Ca/P ratio was significantly lower in the demineralized control, Varnish, and Ult-YF3-nHAP groups than in other experimental groups (p = 0.002). Conclusion Ult-YF3-nHAP showed higher efficacy in remineralization of aWSLs compared to fluoride-based treatments, CPP-ACP, and resin infiltrant. The highest remineralization was detected in the Ult-YF3-nHAP group by micro-CT and QLF analysis, while fluoride varnish gave the lowest result.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, MalikArticle Citation - WoS: 27Citation - Scopus: 32miRcorrNet: 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: 40Citation - Scopus: 49Spatio-Temporal Rich Model-Based Video Steganalysis on Cross Sections of Motion Vector Planes(Institute of Electrical and Electronics Engineers Inc., 2016-07) Taşdemir, K.; Kurugöllü, F.; Sakir Sezer, S.; Sezer, SakirA rich model-based motion vector (MV) steganalysis benefiting from both temporal and spatial correlations of MVs is proposed in this paper. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this paper. First, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring MVs for longer distances. Therefore, temporal MV dependency alongside the spatial dependency is utilized for rigorous MV steganalysis. Second, unlike the filters previously used, which were heuristically designed against a specific MV steganography, a diverse set of many filters, which can capture aberrations introduced by various MV steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in the previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-Temporal range and provides a better cover model. The proposed method is tested against the most prominent MV steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in MV steganalysis field, including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads. © 2016 Elsevier B.V., All rights reserved.Article Achieving Extreme Solubility and Green Solvent-Processed Organic Field-Effect Transistors: A Viable Asymmetric Functionalization of [1]Benzothieno[3,2-B][1]Benzothiophenes(American Chemical Society, 2025-08-22) Yıldız, T.A.; Deneme, İ.; Usta, H.Novel structural engineering strategies for solubilizing high-mobility semiconductors are critical, which enables green solvent processing for eco-friendly, sustainable device fabrication, and unique molecular properties. Here, we introduce a viable asymmetric functionalization approach, synthesizing monocarbonyl [1]benzothieno[3,2-b][1]benzothiophene molecules on a gram scale in two transition-metal-free steps. An unprecedented solubility of up to 176.0 mg·mL–1(at room temperature) is achieved, which is the highest reported to date for a high-performance organic semiconductor. The single-crystal structural analysis reveals a herringbone motif with multiple edge-to-face interactions and nonclassical hydrogen bonds involving the carbonyl unit. The asymmetric backbones adopt an antiparallel arrangement, enabling face-to-face π-π interactions. The mono(alkyl-aryl)carbonyl-BTBT compound, m-C<inf>6</inf>PhCO-BTBT enables formulations in varied green solvents, including acetone and ethanol, all achieving p-channel top-contact/bottom-gate OFETs in ambient conditions. Charge carrier mobilities of up to 1.87 cm2/V·s (μ<inf>eff</inf>≈ 0.4 cm2/V·s; I<inf>on</inf>/I<inf>off</inf>≈ 107–108) were achieved. To the best of our knowledge, this is one of the highest OFET performances achieved using a green solvent. Hansen solubility parameters (HSP) analysis, combined with Scatchard–Hildebrand regular solution theory and single-crystal packing analysis, elucidates this exceptional solubility and reveals unique relationships between molecular structure, interaction energy densities, cohesive energetics, and solute–solvent distances (R<inf>a</inf>). An optimal solute–green solvent interaction distance in HSP space proves critical for green solvent-processed thin-film properties. This asymmetric functionalization approach, with demonstrated unique solubility insights, provides a foundation for designing green solvent-processable π-conjugated systems, potentially advancing innovation in sustainable (opto)electronics and bioelectronics. © 2025 Elsevier B.V., All rights reserved.