Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
5 results
Search Results
Article Dissecting the Metabolic Landscape of Breast Cancer Subtypes via Elastic Net Modeling and Examining Its Immune Correlates(Walter de Gruyter GmbH, 2026) Kus, M.E.; Ekiz, H.A.Objectives: Breast cancer is a heterogeneous disease, and the estrogen receptor (ER) status is a key factor in disease classification and treatment planning. While metabolomic profiling has revealed subtype-specific differences, cross-study comparisons have been limited, posing challenges for data extrapolation. This study aims to investigate metabolites that differentiate ER-positive and ER-negative tumors via integrative analyses of multi-omics data. Methods: We jointly analyzed two untargeted metabolomics datasets via elastic net modeling using consistent analysis pipelines tuned for low sample sizes, namely multiple bootstrapping and stability selection. Significant metabolite predictors from two studies were cross-examined to reveal distinctions and commonalities. We also performed differential gene expression analysis using RNA sequencing data from matching samples to link metabolic patterns with transcriptomic signatures and intratumoral immune cell signatures. Results: This study identified unique metabolite signatures in distinct datasets and a limited overlap of discriminating metabolites that can be broadly generalizable for subtyping. Nevertheless, several glycolysis and fatty acid metabolism intermediates exhibited variation depending on the tumor ER status. Consistently, genes related to fatty acid metabolism and glycolysis were enriched in ER-positive and ER-negative tumors respectively. Furthermore, we used multiple immune cell deconvolution algorithms to correlate various immune cell types with the metabolite levels within the tumor microenvironment. Conclusions: Together, these findings highlight the metabolic and immunological diversity of breast cancer and establish a reproducible machine-learning framework for integrating multi-omics data to interrogate tumor complexity. © 2025 the author(s), published by De Gruyter, Berlin/Boston.Article Citation - WoS: 21Citation - Scopus: 22Effects of Spaceflight on Cells of Bone Marrow Origin(Aves, 2013) Özçivici, EnginOnce only a subject for science fiction novels, plans for establishing habitation on space stations, the Moon, and distant planets now appear among the short-term goals of space agencies. This article reviews studies that present biomedical issues that appear to challenge humankind for long-term spaceflights. With particularly focus on cells of bone marrow origin, studies involving changes in bone, immune, and red blood cell populations and their functions due to extended weightlessness were reviewed. Furthermore, effects of mechanical disuse on primitive stem cells that reside in the bone marrow were also included in this review. Novel biomedical solutions using space biotechnology will be required in order to achieve the goal of space exploration without compromising the functions of bone marrow, as spaceflight appears to disrupt homeostasis for all given cell types.Article Citation - WoS: 2Citation - Scopus: 2Trait-based heterogeneous populations plus (TbHP+) genetic algorithm(Elsevier Ltd., 2009) Tayfur, Gökmen; Sevil, Hakkı Erhan; Gezgin, Erkin; Özdemir, SerhanThis study developed a variant of genetic algorithm (GA) model called the trait-based heterogeneous populations plus (TbHP+). The developed TbHP+ model employs a memory concept in the form of immunity and instinct to provide the populations with a more efficient guidance. Also, it has an ability to vary the number of individuals during the search process, thus allowing an automatic determination of the size of the population based on the individual qualities such as character fitness and credit for immunity. The algorithm was tested against the classical GA model in convergence and minimum error performance. For this purpose, 5 different mathematical functions from the literature were employed. The selected functions have different topological characteristics, ranging from simple convex curves with 2 variables to complex trigonometric ones having several hilly shapes with more than 2 variables. The developed model and the classical GA model were applied to finding the global minima of the functions. The comparison of the results revealed that the developed TbHP+ model outperformed the classical GA in faster convergence and minimum errors, which may be explained by the adaptive nature of the new paradigm.Article Citation - Scopus: 2Identification of Potato Y Potyvirus (pvy°) Resistance in Wild and Cultivated Tomatoes(Türkiye Klinikleri Journal of Medical Sciences, 2009) Çelebi Toprak, Fevziye; Barutçu, Eminur; Frary, Anne; Doğanlar, SamiPotato Y potyvirus (PVY) is an important plant pathogen worldwide that infects and causes yield losses in the family Solanaceae including potato (Solarium tuberosum), pepper (Capsicum spp.), tomato (S. lycopersicum), and tobacco (Nicotiana tabacum). In this study, 20 different tomato accessions representing 6 different species were mechanically inoculated with PVY°. The plants were scored visually for symptoms and then tested for presence of the virus 2-4 weeks after inoculation by ELISA. The results were variable. Most wild species of tomato sustained PVY° replication in inoculated leaves. Some of the wild species showed an immune response, while some became systemically infected. Inoculation and analysis of F2 populations suggested that the resistance is controlled by a single recessive gene in different wild species.Conference Object Citation - Scopus: 1The Effects of Bias, Population Migration and Credit Assignment in Optimizing Trait-Based Heterogeneous Populations(CSREA Press, 2005) Gezgin, Erkin; Sevil, Hakkı Erhan; Özdemir, SerhanPopulation based search algorithms are becoming the mainstay in nonlinear problems with discontinuous search domains. The generic name of genetic algorithms (GAs) basicly applies to all population based methods. GAs have spawned many versions to suit new applications. Some of these alterations have reached such points that the algorithms may no longer be called GAs. One similar study may be found in [1], in which a perturbation based search algorithm was proposed, called Responsive Perturbation Algorithm (RPA). In a later work [2], instead of a population of homogenous individuals, as is the case for generic GAs, a population of heterogeneous individuals has been set to compete. Replacing the set of winner parents, the fittest individual is made the parent to yield offspring. The current work is now called, with the supplements, trait-based heterogeneous populations plus (TbHP+). Credit assignment and bias concepts in the form of immunity and instinct has been added to provide the populations with a more efficient guidance. Simulations were made through an RBF neural network training, as it was carried out in earlier works, mentioned above, for comparison. Results were prsented at the end as network testing errors which showed further improvement with TbHP+.