WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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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: 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: 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.Article Citation - WoS: 1Citation - Scopus: 1PSO Supported Ensemble Algorithm for Bad Data Detection Against Intelligent Hacking Algorithm(Frontiers Media S.A., 2021-07-23) Yavuz, Levent; Soran, Ahmet; Onen, Ahmet; Muyeen, S. M.Power system cybersecurity has recently become important due to cyber-attacks. Due to advanced computer science and machine learning (ML) applications being used by malicious attackers, cybersecurity is becoming crucial to creating sustainable, reliable, efficient, and well-protected cyber-systems. Power system operators are needed to develop sophisticated detection mechanisms. In this study, a novel machine-learning-based detection algorithm that combines the five most popular ML algorithms with Particle Swarm Optimizer (PSO) is developed and tested by using an intelligent hacking algorithm that is specially developed to measure the effectiveness of this study. The hacking algorithm provides three different types of injections: random, continuous random, and slow injections by adaptive manner. This would make detection harder. Results shows that recall values with the proposed algorithm for each different type of attack have been increased.Article Citation - WoS: 6Citation - Scopus: 10Optimization of Multiple Battery Swapping Stations With Mobile Support for Ancillary Services(Frontiers Media S.A., 2022-09-26) Kocer, Mustafa Cagatay; Onen, Ahmet; Ustun, Taha Selim; Albayrak, SahinThe recent developments in electric vehicles (EVs) causes several issues that have not been satisfactorily addressed. One of the foremost problems is the charging-discharging processes of EV batteries with diverse characteristics. Although a charging station is the first choice in this regard, a battery swap station (BSS) is also a suitable alternative solution as it eliminates long waiting periods and battery degradation due to fast charging. BSS has the capability to ensure prompt and efficient service for electric vehicles. Since BSS has a large number of battery systems, optimum planning of the charging-discharging operations of the batteries is critical for both BSS and the grid. This study presents an optimal charging-discharging schedule for multiple BSSs based on the swap demand of privately owned EVs and electric bus (EB) public transportation system. In addition, BSSs reinforce the power grid by providing ancillary services such as peak shaving and valley filling with demand response programs. In order to increase the flexibility of the operation, the mobile swapping station (MSS) concept, an innovative and dynamic service, is introduced to the literature and added to the model. The results indicate that BSS is an essential agent in the ancillary services market and the MSS concept is a yielding solution for both BSSs and power networks. Last, the data utilized in the study for swap demand calculation and power grid analysis are real-world data from Berlin, Germany.Article Citation - WoS: 5Citation - Scopus: 7Modulating the Surface Properties of Metallic Implants and the Response of Breast Cancer Cells by Surface Relief Induced via Bulk Plastic Deformation(Frontiers Media S.A., 2020-05-07) Uzer, BenayMicro/nanoscale textured surfaces have presented promising tissue-implant integration via increasing surface roughness, energy, and wettability. Recent studies indicate that surface texture imparted on the metallic implants via surface relief induced with simple bulk plastic deformation methods (e.g., tension or compression tests) does result in enhanced cell response. Considering these recent findings, this study presents a thorough investigation of the effects of surface relief on surface properties of implants and cell adhesion. Experiments are conducted on the samples subjected to interrupted tensile tests up to the plastic strains of 5, 15, 25, and 35%. Main findings from these experiments suggest that, as the plastic deformation level increases up to 35% from the undeformed (control) level, (1) average surface roughness (R-a) increases from 17.58 to 595.29 nm; (2) water contact angle decreases from 84.28 to 58.07 degrees; (3) surface free energy (SFE) increases from 36.06 to 48.89 mJ/m(2); and (4) breast cancer cells show 2.4-fold increased number of attachment. Increased surface roughness indicates the distorted topography via surface relief and leads to increased wettability, consistent with Wenzel's theory. The higher levels of SFE observed were related to high-energy regions provided via activation of strengthening mechanisms, which increased in volume fraction concomitant with plastic deformation. Eventually, the displayed improvements in surface properties have increased the number of breast cancer cell attachments. These findings indicate that surface relief induced upon plastic deformation processes could be utilized in the design of implants for therapeutic or diagnostic purposes through capturing breast cancer cells on the material surface.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 .