WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 8Citation - Scopus: 7Raft-Synthesized Poegma-B Block Copolymers: Preparation of Nanosized Micelles for Anticancer Drug Release(Springer, 2021-11-14) Bayram, Nazende Nur; Topuzogullari, Murat; Isoglu, Ismail Alper; Isoglu, Sevil Dincer; Dinçer İşoğlu, SevilTo achieve high stability and biocompatibility in physiological environment, oligoethyleneglycol methacrylate (OEGMA) and 4-vinylpyridine (4VP)-based amphiphilic block copolymers were prepared as micellar carriers to deliver doxorubicin into tumor cells. First, macroinitiator of OEGMA was synthesized by RAFT polymerization at [M](0)/[CTA](0)/[I](0) ratio of 100/1/0.2 in dimethylformamide (DMF) at 70 degrees C, in the presence of 4,4'-azobis(4-cyanovaleric acid) (ACVA) as initiator and 4-cyano-4-(thiobenzoylthio)pentanoic acid (CTA) as chain transfer agent, respectively. It was followed by copolymerization with 4-VP at similar conditions. The formation of RAFT-mediated polymers was approved by FTIR, H-1-NMR and GPC. For the preparation of drug-loaded micelles, a dialysis method was applied and hydrophobic doxorubicin, as a model drug, was entrapped into the micelles. Size distributions and morphologies of drug-loaded micelles were investigated by light scattering and scanning electron microscopy, respectively. Critical micelle concentration was estimated as 0.0019 mg/mL by measuring light scattering intensity in different polymer concentrations. Also, drug loading and entrapment efficiencies were calculated as 4.41% and 17.65% by measuring the DOX amount in the micelles, spectrophotometrically. At last, the drug-loaded micelles were applied to SKBR-3 breast cancer cell lines and revealed up to %40 cell inhibition at 48 and 72 h. As a result, these nanosized and biocompatible micelles can be used for the delivery of hydrophobic drugs, and they can also be modified for further targeting and imaging applications toward specific cancer cells. [GRAPHICS] .Conference Object Peptide Targeted Core Cross-Linked Micelles for Dox Delivery to HER2 Expressing Cancer Cells(Mary Ann Liebert, inc, 2022) Bayram, Nazende Nur; Ulu, Gizem Tugce; Gurdap, Seda; Isoglu, Ismail Alper; Baran, Yusuf; Isoglu, Sevil DincerConference Object Leveraging MicroRNA-Gene Associations With Mirgedinet: An Intelligent Approach for Enhanced Classification of Breast Cancer Molecular Subtypes(Springer International Publishing AG, 2025) Qumsiyeh, Emma; Bakir-Gungor, Burcu; Yousef, MalikUnderstanding the molecular subtypes of breast cancer is crucial for advancing targeted therapies and precision medicine. For the BRCA molecular subtype prediction problem, this study employs miRGediNET, a machinelearning approach that integrates data from miRTarBase, DisGeNET, and HMDD databases to investigate shared gene associations between microRNA (miRNA) activity and disease mechanisms. Using the BRCA LumAB_Her2Basal dataset, we evaluate miRGediNET's performance against traditional feature selection methods, including CMIM, mRmR, Information Gain (IG), SelectKBest (SKB), Fast Correlation-Based Filter (FCBF), and XGBoost (XGB). These feature selection techniques were assessed using various classification algorithms including Random Forest (RF), Support Vector Machine (SVM), LogitBoost, Decision Tree, and AdaBoost, all executed with default parameters. The feature selection methods were tested using Monte Carlo Cross-Validation, where performance metrics obtained for each iteration were averaged to ensure robustness. Our findings reveal that miRGediNET outperforms traditional methods in accuracy and Area Under the Curve (AUC), emphasizing its superior capability to identify key genes that bridge miRNA interactions and breast cancer mechanisms. Notably, both miRGediNET and Information Gain (IG) feature selection consistently identified ESR1, a critical biomarker frequently reported in recent research associated with breast cancer prognosis and resistance to endocrine therapies. This integrative approach provides deeper biological insights into miRNA-disease interactions, paving the way for enhanced patient stratification, biomarker discovery, and personalized medicine strategies. The miRGediNET tool, developed on the KNIME platform, offers a practical resource for further exploration in the field of bioinformatics and oncology.Article Citation - WoS: 16Citation - Scopus: 20Invention of 3Mint for Feature Grouping and Scoring in Multi-Omics(Frontiers Media S.A., 2023-03-15) Yazici, Miray Unlu; Marron, J. S.; Bakir-Gungor, Burcu; Zou, Fei; Yousef, Malik; Unlu Yazici, MirayAdvanced genomic and molecular profiling technologies accelerated the enlightenment of the regulatory mechanisms behind cancer development and progression, and the targeted therapies in patients. Along this line, intense studies with immense amounts of biological information have boosted the discovery of molecular biomarkers. Cancer is one of the leading causes of death around the world in recent years. Elucidation of genomic and epigenetic factors in Breast Cancer (BRCA) can provide a roadmap to uncover the disease mechanisms. Accordingly, unraveling the possible systematic connections between-omics data types and their contribution to BRCA tumor progression is crucial. In this study, we have developed a novel machine learning (ML) based integrative approach for multi-omics data analysis. This integrative approach combines information from gene expression (mRNA), MicroRNA (miRNA) and methylation data. Due to the complexity of cancer, this integrated data is expected to improve the prediction, diagnosis and treatment of disease through patterns only available from the 3-way interactions between these 3-omics datasets. In addition, the proposed method bridges the interpretation gap between the disease mechanisms that drive onset and progression. Our fundamental contribution is the 3 Multi-omics integrative tool (3Mint). This tool aims to perform grouping and scoring of groups using biological knowledge. Another major goal is improved gene selection via detection of novel groups of cross-omics biomarkers. Performance of 3Mint is assessed using different metrics. Our computational performance evaluations showed that the 3Mint classifies the BRCA molecular subtypes with lower number of genes when compared to the miRcorrNet tool which uses miRNA and mRNA gene expression profiles in terms of similar performance metrics (95% Accuracy). The incorporation of methylation data in 3Mint yields a much more focused analysis. The 3Mint tool and all other supplementary files are available at .Article Citation - WoS: 3Citation - Scopus: 3HER2-Specific Peptide (LTWWYSPY) and Antibody (Herceptin) Targeted Core Cross-Linked Micelles for Breast Cancer: A Comparative Study(MDPI, 2023-02-22) Bayram, Nazende Nur; Ulu, Gizem Tugce; Abdulhadi, Nusaibah Abdulsalam; Guerdap, Seda; Isoglu, Ismail Alper; Baran, Yusuf; Isoglu, Sevil Dincer; Gürdap, SedaThis study aims to prepare a novel breast cancer-targeted micelle-based nanocarrier, which is stable in circulation, allowing intracellular drug release, and to investigate its cytotoxicity, apoptosis, and cytostatic effects, in vitro. The shell part of the micelle is composed of zwitterionic sulfobetaine ((N-3-sulfopropyl-N,N-dimethylamonium)ethyl methacrylate), while the core part is formed by another block, consisting of AEMA (2-aminoethyl methacrylamide), DEGMA (di(ethylene glycol) methyl ether methacrylate), and a vinyl-functionalized, acid-sensitive cross-linker. Following this, a targeting agent (peptide (LTVSPWY) and antibody (Herceptin((R)))), in varying amounts, were coupled to the micelles, and they were characterized by H-1 NMR, FTIR (Fourier-transform infrared spectroscopy), Zetasizer, BCA protein assay, and fluorescence spectrophotometer. The cytotoxic, cytostatic, apoptotic, and genotoxic effects of doxorubicin-loaded micelles were investigated on SKBR-3 (human epidermal growth factor receptor 2 (HER2)-positive) and MCF10-A (HER2-negative). According to the results, peptide-carrying micelles showed a higher targeting efficiency and better cytostatic, apoptotic, and genotoxic activities than antibody-carrying and non-targeted micelles. Also, micelles masked the toxicity of naked DOX on healthy cells. In conclusion, this nanocarrier system has great potential to be used in different drug-targeting strategies, by changing targeting agents and drugs.Article Citation - WoS: 13Citation - Scopus: 14HER2-Targeted, Degradable Core Cross-Linked Micelles for Specific and Dual pH-Sensitive Dox Release(Wiley-VCH Verlag GmbH, 2021-11-09) Bayram, Nazende Nur; Ulu, Gizem Tugce; Topuzogullari, Murat; Baran, Yusuf; Isoglu, Sevil Dincer; Dinçer İşoğlu, SevilHere, a targeted, dual-pH responsive, and stable micelle nanocarrier is designed, which specifically selects an HER2 receptor on breast cancer cells. Intracellularly degradable and stabilized micelles are prepared by core cross-linking via reversible addition-fragmentation chain-transfer (RAFT) polymerization with an acid-sensitive cross-linker followed by the conjugation of maleimide-doxorubicin to the pyridyl disulfide-modified micelles. Multifunctional nanocarriers are obtained by coupling HER2-specific peptide. Formation of micelles, addition of peptide and doxorubicin (DOX) are confirmed structurally by spectroscopical techniques. Size and morphological characterization are performed by Zetasizer and transmission electron microscope (TEM). For the physicochemical verification of the synergistic acid-triggered degradation induced by acetal and hydrazone bond degradation, Infrared spectroscopy and particle size measurements are used. Drug release studies show that DOX release is accelerated at acidic pH. DOX-conjugated HER2-specific peptide-carrying nanocarriers significantly enhance cytotoxicity toward SKBR-3 cells. More importantly, no selectivity toward MCF-10A cells is observed compared to HER2(+) SKBR-3 cells. Formulations cause apoptosis depending on Bax and Caspase-3 and cell cycle arrest in G2 phase. This study shows a novel system for HER2-targeted therapy of breast cancer with a multifunctional nanocarrier, which has higher stability, dual pH-sensitivity, selectivity, and it can be an efficient way of targeted anticancer drug delivery.Article Citation - WoS: 6Citation - Scopus: 9BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models(MDPI, 2021-11-09) Senturk, Niyazi; Tuncel, Gulten; Dogan, Berkcan; Aliyeva, Lamiya; Dundar, Mehmet Sait; Ozemri Sag, Sebnem; Ergoren, Mahmut Cerkez; Sag, Sebnem OzemriArtificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients' data were used to train the system using Mamdani's Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network's overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations' risk assessment in breast cancers as well as a unique tool for personalized medicine software.Article Citation - WoS: 15Citation - Scopus: 14A Review of Mammographic Region of Interest Classification(Wiley Periodicals, inc, 2020-02-18) Yengec Tasdemir, Sena B.; Tasdemir, Kasim; Aydin, ZaferEarly detection of breast cancer is important and highly valuable in clinical practice. X-ray mammography is broadly used for prescreening the breast and is also attractive due to its noninvasive nature. However, experts can misdiagnose a significant proportion of the cases, which may either cause redundant examinations or cancer. In order to reduce false positive and negative rates of mammography screening, computer-aided breast cancer detection has been studied for more than 30 years and many methods have been proposed by the researchers. In this review, region of interest (ROI) classification methods, which operate on a predefined or segmented ROIs with a focus on mass classification are surveyed. A total of 72 high quality journal and conference papers are selected from the Web of Science (WOS) database that meet several inclusion criteria. A comparative analysis is provided based on ROI extraction methods, data sets and machine learning techniques employed, the prediction accuracies, and usage frequency statistics. Based on the performances obtained on publicly available data sets, the ROI classification problem from mammogram images can be considered as approaching to be solved. Nonetheless, it can still be used as complementary information in breast cancer detection from the whole mammograms, which has room for improvement. This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Technologies > ClassificationConference Object Citation - WoS: 8Citation - Scopus: 9Meme Kanseri Histopatolojik Görüntülerinin Bilgisayar Destekli Sınıflandırılması(Institute of Electrical and Electronics Engineers Inc., 2017-10) Aksebzeci, Bekir Hakan; Kayaaltı, ÖmerNowadays, one of the most common types of cancer is breast cancer. The early and accurate diagnosis of breast cancer has great importance in the treatment of the disease. In the diagnosis of breast cancer, histopathological analysis of cell and tissue specimens taken by biopsy is considered as the gold standard. Histopathological analysis is a tedious process that is highly dependent on the knowledge and experience of the pathologists. In this study; it is aimed to develop a computer-Aided system that can reduce the workload of pathologists and help them in their diagnosis. An image set containing benign and malignant tumor images of breast cancer has been studied. To perform texture analysis on tumor images; first order statistics, Gabor and gray-level co-occurrence matrix (GLCM) feature extraction methods have been applied. Then, various classifiers were applied to the obtained feature matrices and their performances were compared. The highest classification accuracy was achieved 82.06% by Random Forests classifier with feature combination of Gabor and GLCM methods. The results presented here show that computer-Assisted diagnosis of breast cancer is a promising field. © 2018 Elsevier B.V., All rights reserved.