PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
Browse
3 results
Search Results
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: 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.