Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
28 results
Search Results
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: 26Citation - Scopus: 33miRmoduleNet: Detecting miRNA-mRNA Regulatory Modules(Frontiers Media S.A., 2022-04-12) Yousef, Malik; Goy, Gokhan; Bakir-Gungor, BurcuIncreasing evidence that MicroRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis.Article Citation - WoS: 20Citation - Scopus: 24miRdisNET: Discovering MicroRNA Biomarkers That Are Associated With Diseases Utilizing Biological Knowledge-Based Machine Learning(Frontiers Media S.A., 2023-01-12) Jabeer, Amhar; Temiz, Mustafa; Bakir-Gungor, Burcu; Yousef, MalikDuring recent years, biological experiments and increasing evidence have shown that MicroRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA associations as input files. First, we generate subsets of specific diseases by applying the grouping component. These subsets contain miRNA expressions with class labels associated with each specific disease. Then, we assign an importance score to each group by using a machine learning method for classification. Finally, we apply a modeling component and obtain outputs. One of the most important outputs of miRdisNET is the performance of miRNA-disease prediction. Compared with the existing methods, miRdisNET obtained the highest AUC value of .9998. Another output of miRdisNET is a list of significant miRNAs for disease under study. The miRNAs identified by miRdisNET are validated via referring to the gold-standard databases which hold information on experimentally verified MicroRNA-disease associations. miRdisNET has been developed to predict candidate miRNAs for new diseases, where miRNA-disease relation is not yet known. In addition, miRdisNET presents candidate disease-disease associations based on shared miRNA knowledge. The miRdisNET tool and other supplementary files are publicly available at: .Article Citation - WoS: 25Citation - Scopus: 33Volume Fraction, Thickness, and Permeability of the Sealing Layer in Microbial Self-Healing Concrete Containing Biogranules(Frontiers Media S.A., 2018-11-23) Ersan, Yusuf Cagatay; Palin, Damian; Tasdemir, Sena Busra Yengec; Tasdemir, Kasim; Jonkers, Henk M.; Boon, Nico; De Belie, Nele; Yengec Tasdemir, Sena BusraAutonomous repair systems in construction materials have become a promising alternative to current unsustainable and labor-intensive maintenance methods. Biomineralization is a popular route that has been applied to enhance the self-healing capacity of concrete. Various axenic microbial cultures were coupled with protective carriers, and their combination appears to be useful for the development of healing agents for realizing self-healing concrete. The advantageous traits of non-axenic cultures, such as economic feasibility, self-protection, and high specific activity have been neglected so far, and thus the number of studies investigating their performance as healing agents is scarce. Here we present the self-healing performance of a mortar containing a healing agent consisting of non-axenic biogranules with a denitrifying core. Mortar specimens with a defined crack width of 400 mu m were used in the experiments and treated with tap water for 28 days. Self-healing was quantified in terms of the crack volume reduction, the thickness of the sealing layer along the crack depth and water permeability under 0.1 bar pressure. Complete visual crack closure was achieved in the bio-based specimens in 28 days, the thickness of the calcite layer was recorded as 10 mm and the healed crack volume was detected as 6%. Upon self-sealing of the specimens, the water permeability decreased by 83%. Overall, non-axenic biogranules with a denitrifying core shows great potential for development of self-healing bioconcrete.Article Citation - WoS: 2Citation - Scopus: 2Transparent Colloidal Crystals With Structural Colours(Frontiers Media S.A., 2022-03-07) Erdem, Talha; O'Neill, Thomas; Zupkauskas, Mykolas; Caciagli, Alessio; Xu, Peicheng; Lan, Yang; Eiser, Erika; O’Neill, ThomasSpatially ordered arrangements of spherical colloids are known to exhibit structural colours. The intensity and brilliance of these structural colours typically improve with colloidal monodispersity, low concentrations of point and line defects and with increasing refractive index contrast between the colloids and the embedding medium. Here we show that suspensions of charge stabilised, fluorinated latex particles with low refractive-index contrast to their aqueous background form Wigner crystals with FCC symmetry for volume fractions between 13 and 40%. In reflection they exhibit both strong, almost angle-independent structural colours and sharp, more brilliant Bragg peaks despite the particle polydispersity and bimodal distribution. Simultaneously, these suspensions appear transparent in transmission. Furthermore, binary AB, A(2)B and A(13)B type mixtures of these fluorinated and similarly sized polystyrene particles appeared predominantly white but with clear Bragg peaks indicating a CsCl-like BCC structure and more complex crystals. We characterised the suspensions using a combination of reflectivity measurements and small-angle x-ray scattering, complemented by reflectivity modelling.Article Citation - WoS: 36Citation - Scopus: 35Trail Promotes the Polarization of Human Macrophages Toward a Proinflammatory M1 Phenotype and Is Associated With Increased Survival in Cancer Patients With High Tumor Macrophage Content(Frontiers Media S.A., 2023-09-21) Gunalp, Sinem; Helvaci, Derya Goksu; Oner, Aysenur; Bursali, Ahmet; Conforte, Alessandra; Guener, Hueseyin; Sag, Duygu; Güner, HüseyinBackgroundTNF-related apoptosis-inducing ligand (TRAIL) is a member of the TNF superfamily that can either induce cell death or activate survival pathways after binding to death receptors (DRs) DR4 or DR5. TRAIL is investigated as a therapeutic agent in clinical trials due to its selective toxicity to transformed cells. Macrophages can be polarized into pro-inflammatory/tumor-fighting M1 macrophages or anti-inflammatory/tumor-supportive M2 macrophages and an imbalance between M1 and M2 macrophages can promote diseases. Therefore, identifying modulators that regulate macrophage polarization is important to design effective macrophage-targeted immunotherapies. The impact of TRAIL on macrophage polarization is not known.MethodsPrimary human monocyte-derived macrophages were pre-treated with either TRAIL or with DR4 or DR5-specific ligands and then polarized into M1, M2a, or M2c phenotypes in vitro. The expression of M1 and M2 markers in macrophage subtypes was analyzed by RNA sequencing, qPCR, ELISA, and flow cytometry. Furthermore, the cytotoxicity of the macrophages against U937 AML tumor targets was assessed by flow cytometry. TCGA datasets were also analyzed to correlate TRAIL with M1/M2 markers, and the overall survival of cancer patients.ResultsTRAIL increased the expression of M1 markers at both mRNA and protein levels while decreasing the expression of M2 markers at the mRNA level in human macrophages. TRAIL also shifted M2 macrophages towards an M1 phenotype. Our data showed that both DR4 and DR5 death receptors play a role in macrophage polarization. Furthermore, TRAIL enhanced the cytotoxicity of macrophages against the AML cancer cells in vitro. Finally, TRAIL expression was positively correlated with increased expression of M1 markers in the tumors from ovarian and sarcoma cancer patients and longer overall survival in cases with high, but not low, tumor macrophage content.ConclusionsTRAIL promotes the polarization of human macrophages toward a proinflammatory M1 phenotype via both DR4 and DR5. Our study defines TRAIL as a new regulator of macrophage polarization and suggests that targeting DRs can enhance the anti-tumorigenic response of macrophages in the tumor microenvironment by increasing M1 polarization.Article Citation - WoS: 23Citation - Scopus: 23The Age Structure, Stringency Policy, Income, and Spread of Coronavirus Disease 2019: Evidence From 209 Countries(Frontiers Media S.A., 2021-02-12) Bilgili, Faik; Dundar, Munis; Kuskaya, Sevda; Lorente, Daniel Balsalobre; Unlu, Fatma; Gencoglu, Pelin; Mugaloglu, ErhanThis article aims at answering the following questions: (1) What is the influence of age structure on the spread of coronavirus disease 2019 (COVID-19)? (2) What can be the impact of stringency policy (policy responses to the coronavirus pandemic) on the spread of COVID-19? (3) What might be the quantitative effect of development levelincome and number of hospital beds on the number of deaths due to the COVID-19 epidemic? By employing the methodologies of generalized linear model, generalized moments method, and quantile regression models, this article reveals that the shares of median age, age 65, and age 70 and older population have significant positive impacts on the spread of COVID-19 and that the share of age 70 and older people in the population has a relatively greater influence on the spread of the pandemic. The second output of this research is the significant impact of stringency policy on diminishing COVID-19 total cases. The third finding of this paper reveals that the number of hospital beds appears to be vital in reducing the total number of COVID-19 deaths, while GDP per capita does not affect much the level of deaths of the COVID-19 pandemic. Finally, this article suggests some governmental health policies to control and decrease the spread of COVID-19.Article Citation - WoS: 10Citation - Scopus: 15Textnettopics Pro, a Topic Model-Based Text Classification for Short Text by Integration of Semantic and Document-Topic Distribution Information(Frontiers Media S.A., 2023-10-05) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikWith the exponential growth in the daily publication of scientific articles, automatic classification and categorization can assist in assigning articles to a predefined category. Article titles are concise descriptions of the articles' content with valuable information that can be useful in document classification and categorization. However, shortness, data sparseness, limited word occurrences, and the inadequate contextual information of scientific document titles hinder the direct application of conventional text mining and machine learning algorithms on these short texts, making their classification a challenging task. This study firstly explores the performance of our earlier study, TextNetTopics on the short text. Secondly, here we propose an advanced version called TextNetTopics Pro, which is a novel short-text classification framework that utilizes a promising combination of lexical features organized in topics of words and topic distribution extracted by a topic model to alleviate the data-sparseness problem when classifying short texts. We evaluate our proposed approach using nine state-of-the-art short-text topic models on two publicly available datasets of scientific article titles as short-text documents. The first dataset is related to the Biomedical field, and the other one is related to Computer Science publications. Additionally, we comparatively evaluate the predictive performance of the models generated with and without using the abstracts. Finally, we demonstrate the robustness and effectiveness of the proposed approach in handling the imbalanced data, particularly in the classification of Drug-Induced Liver Injury articles as part of the CAMDA challenge. Taking advantage of the semantic information detected by topic models proved to be a reliable way to improve the overall performance of ML classifiers.Article Citation - WoS: 80Citation - Scopus: 93Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions(Frontiers Media S.A., 2021-02-22) Moreno-Indias, Isabel; Lahti, Leo; Nedyalkova, Miroslava; Elbere, Ilze; Roshchupkin, Gennady; Adilovic, Muhamed; Claesson, Marcus J.The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.Article Citation - WoS: 28Citation - Scopus: 31Proteomic and Biological Analysis of the Effects of Metformin Senomorphics on the Mesenchymal Stromal Cells(Frontiers Media S.A., 2021-10-05) Acar, Mustafa Burak; Ayaz-Guner, Serife; Gunaydin, Zeynep; Karakukcu, Musa; Peluso, Gianfranco; Di Bernardo, Giovanni; Galderisi, UmbertoSenotherapeutics are new drugs that can modulate senescence phenomena within tissues and reduce the onset of age-related pathologies. Senotherapeutics are divided into senolytics and senomorphics. The senolytics selectively kill senescent cells, while the senomorphics delay or block the onset of senescence. Metformin has been used to treat diabetes for several decades. Recently, it has been proposed that metformin may have anti-aging properties as it prevents DNA damage and inflammation. We evaluated the senomorphic effect of 6 weeks of therapeutic metformin treatment on the biology of human adipose mesenchymal stromal cells (MSCs). The study was combined with a proteome analysis of changes occurring in MSCs' intracellular and secretome protein composition in order to identify molecular pathways associated with the observed biological phenomena. The metformin reduced the replicative senescence and cell death phenomena associated with prolonged in vitro cultivation. The continuous metformin supplementation delayed and/or reduced the impairment of MSC functions as evidenced by the presence of three specific pathways in metformin-treated samples: 1) the alpha-adrenergic signaling, which contributes to regulation of MSCs physiological secretory activity, 2) the signaling pathway associated with MSCs detoxification activity, and 3) the aspartate degradation pathway for optimal energy production. The senomorphic function of metformin seemed related to its reactive oxygen species (ROS) scavenging activity. In metformin-treated samples, the CEBPA, TP53 and USF1 transcription factors appeared to be involved in the regulation of several factors (SOD1, SOD2, CAT, GLRX, GSTP1) blocking ROS.
- «
- 1 (current)
- 2
- 3
- »