WoS İndeksli Yayınlar Koleksiyonu

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  • Conference Object
    Security Through Digital Twin-Based Intrusion Detection: A Swat Dataset Analysis
    (IEEE, 2023-10-18) Bozdal, Mehmet
    Digital twin, as a virtual replica of physical entity, offer valuable insights into Industrial Control System (ICS) behavior and characteristics. Leveraging the convergence of digital twins and cybersecurity, this research explores its role in securing critical infrastructure, using the Secure Water Treatment (SWaT) system as a case study. Existing intrusion detection systems (IDS) for SWaT encounter challenges related to requiring huge amounts of a dataset for training, being unable to adopt high data dimensionality, and adaptability to emerging threats. To address these issues, a hybrid digital twin model is proposed, combining physics-based models and data-driven approaches. This model facilitates precise attack localization and explainable IDS outcomes. The method exhibits promising capabilities for enhancing critical infrastructure security and adapting to evolving cyber threats. Experimental results demonstrate the ability to detect eight out of nine attack types.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 5
    Π-Conjugated Donor-Acceptor Small Molecule Thin-Films on Gold Electrodes for Reducing the Metal Work-Function
    (Elsevier Science SA, 2016-10) Azum, Naved; Taib, Layla Ahmad; Al Angari, Yasser Mohammed; Asiri, Abdullah M.; Denti, Mitchel; Zhao, Wei; Facchetti, Antonio
    This paper reports the design, facile synthesis and purification of four pi-conjugated donor-acceptor small molecules comprising heteroaromatic units, DA-1-DA-4, for surface and electronic structure modification of gold thin film. These molecules were characterized by H-1/C-13 nuclear magnetic resonance spectroscopy, cyclic voltammetry, UV-Vis spectroscopy, and single-crystal X-ray diffraction. Morphologically smooth thin-films (similar to 5 nm) of DA-1-DA-4 were deposited onto Au thin films via thermal evaporation and characterized by atomic force microscopy, theta-2 theta X-ray diffraction and ultraviolet photoelectron spectroscopy. The work functions of the small molecule coated Au electrodes are shifted to lower energies by similar to 0.1-03 eV, compared to that of the bare Au film measured as a reference. The vapor-deposition of structurally,simple small molecules developed here shows great promise as a facile approach to reduce gold work function for electron injection/extraction between organic semiconductors and Au contacts in various opto-electronic devices. (C) 2016 Elsevier B.V. All tights reserved.
  • Article
    Citation - WoS: 38
    Citation - Scopus: 38
    pH- and Temperature-Responsive Amphiphilic Diblock Copolymers of 4-Vinylpyridine and Oligoethyleneglycol Methacrylate Synthesized by RAFT Polymerization
    (Elsevier Sci Ltd, 2014-01) Topuzogullari, Murat; Bulmus, Volga; Dalgakiran, Eray; Dincer, Sevil
    Diblock copolymers of 4-vinylpyridine (4VP) and oligoethyleneglycol methyl ether methacrylate (OEGMA) were synthesized for the first time using RAFT polymerization technique as potential drug delivery systems. Effects of the number of ethylene glycol units in OEGMA, chain length of hydrophobic P4VP block, pH, concentration and temperature on the solution behavior of the copolymers were investigated comprehensively. Copolymer chains formed micelles at pH values higher than 5 whereas unimeric polymers were observed to exist below pH 5, owing to the repulsion between positively charged P4VP blocks. The size of the micelles was dependent on the relative length of blocks, P4VP and POEGMA. Thermo-responsive properties of copolymers were investigated depending on the pH and length of P4VP block. The increase in the length of P4VP block decreased the LCST substantially at pH 7. At pH 3, LCST of copolymers shifted to higher temperatures due to the increased interaction of copolymers with water through positively charged P4VP block. (C) 2013 Elsevier Ltd. All rights reserved.
  • Article
    Citation - WoS: 26
    Citation - Scopus: 33
    miRmoduleNet: Detecting miRNA-mRNA Regulatory Modules
    (Frontiers Media S.A., 2022-04-12) Yousef, Malik; Goy, Gokhan; Bakir-Gungor, Burcu
    Increasing 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: 20
    Citation - Scopus: 24
    miRdisNET: 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, Malik
    During 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: 26
    Citation - Scopus: 31
    miRcorrNet: Machine Learning-Based Integration of miRNA and mRNA Expression Profiles, Combined with Feature Grouping and Ranking
    (PeerJ Inc., 2021-05-19) Yousef, M.; Göy, G.; Mitra, R.; Eischen, C.M.; Jabeer, A.; Bakir-Güngör, B.
    A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/ malikyousef/miRcorrNet. © 2021 Elsevier B.V., All rights reserved.
  • Article
    Citation - Scopus: 1
    eTNT: Enhanced Textnettopics With Filtered LDA Topics and Sequential Forward / Backward Topic Scoring Approaches
    (Science and Information Organization, 2024) Voskergian, Daniel; Jayousi, Rashid; Bakir-Güngör, Burcu
    TextNetTopics is a novel text classification-based topic modelling approach that focuses on topic selection rather than individual word selection to train a machine learning algorithm. However, one key limitation of TextNetTopics is its scoring component, which evaluates each topic in isolation and ranks them accordingly, ignoring the potential relationships between topics. In addition, the chosen topics may contain redundant or irrelevant features, potentially increasing the feature set size and introducing noise that can degrade the overall model performance. To address these limitations and improve the classification performance, this study introduces an enhancement to TextNetTopics. eTNT integrates two novel scoring approaches: Sequential Forward Topic Scoring (SFTS) and Sequential Backward Topic Scoring (SBTS), which consider topic interactions by assessing sets of topics simultaneously. Moreover, it incorporates a filtering component that aims to enhance topics' quality and discriminative power by removing non-informative features from each topic using Random Forest feature importance values. These integrations aim to streamline the topic selection process and enhance classifier efficiency for text classification. The results obtained from the WOS-5736, LitCovid, and MultiLabel datasets provide valuable insights into the superior effectiveness of eTNT compared to its counterpart, TextNetTopics. © 2024 Elsevier B.V., All rights reserved.
  • Conference Object
    Citation - WoS: 49
    Citation - Scopus: 54
    You May Not Reap What You Sow: How Employees' Moral Awareness Minimizes Ethical Leadership's Positive Impact on Workplace Deviance
    (Springer, 2017-08-02) Gok, Kubilay; Sumanth, John J.; Bommer, William H.; Demirtas, Ozgur; Arslan, Aykut; Eberhard, Jared; Yigit, Ahmet
    Although a growing body of research has shown the positive impact of ethical leadership on workplace deviance, questions remain as to whether its benefits are consistent across all situations. In this investigation, we explore an important boundary condition of ethical leadership by exploring how employees' moral awareness may lessen the need for ethical leadership. Drawing on substitutes for leadership theory, we suggest that when individuals already possess a heightened level of moral awareness, ethical leadership's role in reducing deviant actions may be reduced. However, when individuals lack this strong moral disposition, ethical leadership may be instrumental in inspiring them to reduce their deviant actions. To enhance the external validity and generalizability of our findings, the current research used two large field samples of working professionals in both Turkey and the USA. Results suggest that ethical leadership's positive influence on workplace deviance is dependent upon the individual's moral awareness-helpful for those employees whose moral awareness is low, but not high. Thus, our investigation helps to build theory around the contingencies of ethical leadership and the specific audience for whom it may be more (or less) influential.
  • Letter
    Citation - WoS: 18
    Yemen's Triple Emergency: Food Crisis Amid a Civil War and COVID-19 Pandemic
    (Elsevier, 2021-11) Hashim, Hashim Talib; Miranda, Adriana Viola; Babar, Maryam Salma; Essar, Mohammad Yasir; Hussain, Hasham; Ahmad, Shoaib; Basalilah, Ashraf Fhed Mohammed
    Yemen has been termed as the world's worst humanitarian crisis by the United Nations. About 20.1 million (more than 50% of population) Yemenis are facing hunger and 10 million are severely food insecure according to reports by the World Food Programme. With the spread of COVID-19, the situation in Yemen has worsened and humanitarian aid from other countries has become the basis of life for hundreds of thousands of Yemenis after the threat of famine. Yemen is practically one of the poorest countries in the world. It has structural vulnerabilities that have developed over a protracted period of conflict and poor governance and more than 50% live in starving, they suffer for getting one meal a day. To prevent a total collapse of Yemen's food crises, the government and the international community should act now more decisively.
  • Article
    Citation - WoS: 8
    Citation - Scopus: 8
    Writing Chemical Patterns Using Electrospun Fibers as Nanoscale Inkpots for Directed Assembly of Colloidal Nanocrystals
    (Royal Soc Chemistry, 2020) Kiremitler, N. Burak; Torun, Ilker; Altintas, Yemliha; Patarroyo, Javier; Demir, Hilmi Volkan; Puntes, Victor F.; Onses, M. Serdar
    Applications that range from electronics to biotechnology will greatly benefit from low-cost, scalable and multiplex fabrication of spatially defined arrays of colloidal inorganic nanocrystals. In this work, we present a novel additive patterning approach based on the use of electrospun nanofibers (NFs) as inkpots for end-functional polymers. The localized grafting of end-functional polymers from spatially defined nanofibers results in covalently bound chemical patterns. The main factors that determine the width of the nanopatterns are the diameter of the NF and the extent of spreading during the thermal annealing process. Lowering the surface energy of the substrates via silanization and a proper choice of the grafting conditions enable the fabrication of nanoscale patterns over centimeter length scales. The fabricated patterns of end-grafted polymers serve as the templates for spatially defined assembly of colloidal metal and metal oxide nanocrystals of varying sizes (15 to 100 nm), shapes (spherical, cube, rod), and compositions (Au, Ag, Pt, TiO2), as well as semiconductor quantum dots, including the assembly of semiconductor nanoplatelets.