Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Doctoral Thesis A reliable and secure communication design for underwater sensor networks concerning energy efficiency(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) UYAN, Osman Gökhan; Güngör, Vehbi ÇağrıUnderwater Acoustic Sensor Networks (UASNs) recently attract scientists because of its wide range of applications and emerging technology. A design challenge in UASN's is the limited network lifetime and poor reliability caused by limited battery supply of sensors and harsh channel conditions in underwater environment. Moreover, sensors might transmit sensitive data that must be disguised against eavesdropping attacks. To maintain a reliability level, packet-duplication and multi-path routing method are suggested, which renders eavesdropping attacks easier. For data security, cryptographic encryption is the most acclaimed method. However, encryption needs extra computations, which consume extra energy and cause a decrease in the network lifetime. As a countermeasure along with encryption against silent listening, fragmenting data and transmitting in pieces over different paths has been proposed. To address these challenges, an optimization framework has been developed to analyze the effects of multi-path routing, packet duplication, encryption, and data fragmentation on network lifetime. However, the solution time of the proposed optimization model is quite high, and sometimes it cannot come up with feasible solutions. To this end, in this study, different regression and neural network methods have been proposed to predict the energy consumptions of underwater nodes as supplementary methods to optimization models. Performance evaluations show that the proposed methods yield remarkably accurate predictions and can be used for energy consumption prediction in UASNs.Article 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: 21Citation - Scopus: 25miRdisNET: 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: .Conference Object Citation - Scopus: 2miRcorrNetPro: Unraveling Algorithmic Insights Through Cross-Validation in Multi-Omics Integration for Comprehensive Data Analysis(Institute of Electrical and Electronics Engineers Inc., 2023-12-05) Ünlü Yazici, Miray; Yousef, Malik; Marron, J. S.; Bakir-Güngör, Burcu; Yazici, Miray UnluHigh throughput -omics technologies facilitate the investigation of regulatory mechanisms of complex diseases. Along this line, scientists develop promising tools and methods to extend our understanding at the molecular and functional levels. To this end, miRcorrNet tool performs integrative analysis of MicroRNA (miRNA) and gene expression profiles via machine learning (ML) approach to identify significant miRNA groups and their associated target genes. In this study, we propose miRcorrNetPro tool, which extends miRcorrNet by tracking group scoring, ranking and other information through the cross-validation iterations. Heatmap visualizations enable deep novel insights into the collective behavior of clusters of groups in cellular signaling and hence facilitate detection of potential biomarkers for the disease under investigation. Although miRcorrNetPro is designed as a generic tool, here we present our findings and potential miRNA biomarkers for Breast Cancer (BRCA). The miRcorrNetPro tool and all other supplementary files are available at https://github.com/Miray-Unlu/miRcorrNetPro. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 1eTNT: Enhanced Textnettopics With Filtered LDA Topics and Sequential Forward / Backward Topic Scoring Approaches(Science and Information Organization, 2024) Voskergian, Daniel; Jayousi, Rashid; Bakir-Güngör, BurcuTextNetTopics 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.Article Citation - Scopus: 6Wind Turbine Inspection With Drone: Advantages and Disadvantages(Erol Kurt, 2023-03-31) Tanrıverdi, Harun; Karakuş, Güzide; Ulukan, AhmetThe facilities on wind energy generation are increasingly finding usage areas in line with the ecologically friendly energy generation approach. One of the important activities of wind power generation facilities, which have high investment cost, low operating cost and low environmental impact is the maintenance and repair of wind turbines. A preventive maintenance approach is dominant to reduce maintenance times and eliminate lost time in wind turbines. Damage inspection of turbines has been evolved from tower crane access, rope access, camera viewing, and other applications to image with manual drones over the years. However, when these methods are evaluated within the framework of criteria such as cost, performance, occupational safety and data reliability, they are still insufficient and the need for inspection with autonomous drones arises. The advantages and disadvantages of autonomous drones used in the determination of damage in wind turbines are analyzed and the results are considered to contribute to the practitioners operating in the sector and academicians working in the field. © 2023 Elsevier B.V., All rights reserved.Article Citation - WoS: 33Citation - Scopus: 37Wind Farm Site Selection Using GIS-Based Multicriteria Analysis With Life Cycle Assessment Integration(Springer Heidelberg, 2024-01-19) Demir, Abdullah; Dincer, Ali Ersin; Ciftci, Cihan; Gulcimen, Sedat; Uzal, Nigmet; Yilmaz, KutayThe sustainability of wind power plants depends on the selection of suitable installation locations, which should consider not only economic and technical factors including manufacturing and raw materials, but also issues pertaining to the environment. In the present study, a novel methodology is proposed to determine the suitable locations for wind turbine farms by analyzing from the environmental perspective. In the methodology, the life cycle assessment (LCA) of wind turbines is incorporated into the decision process. The criteria are ranked using analytical hierarchy process (AHP). The study area is chosen as the western region of Turkiye. The obtained suitability map reveals that wind speed is not the sole criterion for selecting a site for wind turbine farms; other factors, such as bird migration paths, distance from urban areas and land use, are also crucial. The results also reveal that constructing wind power plants in the vicinity of Izmir, canakkale, Istanbul, and Balikesir in Turkiye can lead to a reduction in emissions. Izmir and its surrounding area show the best environmental performance with the lowest CO2 per kilowatt-hour (7.14 g CO2 eq/kWh), to install a wind turbine due to its proximity to the harbor and steel factory across the study area. canakkale and the northwest region of Turkiye, despite having high wind speeds, are less environmentally favorable than Izmir, Balikesir, and Istanbul. The findings of LCA reveal that the nacelle and rotor components of the wind turbine contribute significantly (43-97%) to the environmental impact categories studied, while the tower component (0-36%) also has an impact.Article Citation - WoS: 7Citation - Scopus: 8Whether and When Did Bitcoin Sentiment Matter for Investors? Before and During the COVID-19 Pandemic(Springer, 2023-12-21) Aysan, Ahmet Faruk; Mugaloglu, Erhan; Polat, Ali Yavuz; Tekin, HasanUsing a wavelet coherence approach, this study investigates the relationship between Bitcoin return and Bitcoin-specific sentiment from January 1, 2016 to June 30, 2021, covering the COVID-19 pandemic period. The results reveal that before the pandemic, sentiment positively drove prices, especially for relatively higher frequencies (2-18 weeks). During the pandemic, the relationship was still positive, but interestingly, the lead-lag relationship disappeared. Employing partial wavelet tools, we factor out the number of COVID-19 cases and deaths and the Equity Market Volatility Infectious Disease Tracker index to observe the direct relationship between a change in sentiment and return. Our results robustly reveal that, before the pandemic, sentiment had a positive effect on return. Although positive coherence still existed during the pandemic, the lead-lag relationship disappeared again. Thus, the causal relationship that states that sentiment leads to return can only be integrated into short-term trading strategies (up to six weeks frequency).Article Citation - WoS: 1Citation - Scopus: 2WSA-Supplements and Proper Classes(MDPI, 2022-08-17) Demirci, Yilmaz Mehmet; Turkmen, ErgulIn this paper, we introduce the concept of wsa-supplements and investigate the objects of the class of short exact sequences determined by wsa-supplement submodules, where a submodule U of a module M is called a wsa-supplement in M if there is a submodule V of M with U + V = M and U boolean AND V is weakly semiartinian. We prove that a module M is weakly semiartinian if and only if every submodule of M is a wsa-supplement in M. We introduce CC-rings as a generalization of C-rings and show that a ring is a right CC-ring if and only if every singular right module has a crumbling submodule. The class of all short exact sequences determined by wsa-supplement submodules is shown to be a proper class which is both injectively and co-injectively generated. We investigate the homological objects of this proper class along with its relation to CC-rings.Article Citation - WoS: 6Citation - Scopus: 6WDR31 Displays Functional Redundancy With GTpase-Activating Proteins (GAPs) ELMOD and RP2 in Regulating Ift Complex and Recruiting the BBsome to Cilium(Life Science Alliance Llc, 2023-05-19) Cevik, Sebiha; Peng, Xiaoyu; Beyer, Tina; Pir, Mustafa S.; Yenisert, Ferhan; Woerz, Franziska; Kaplan, Oktay, IThe correct intraflagellar transport (IFT) assembly at the ciliary base and the IFT turnaround at the ciliary tip are key for the IFT to perform its function, but we still have poor understanding about how these processes are regulated. Here, we identify WDR31 as a new ciliary protein, and analysis from zebrafish and Caeno-rhabditis elegans reveals the role of WDR31 in regulating the cilia morphology. We find that loss of WDR-31 together with RP-2 and ELMD-1 (the sole ortholog ELMOD1-3) results in ciliary accumu-lations of IFT Complex B components and KIF17 kinesin, with fewer IFT/BBSome particles traveling along cilia in both anterograde and retrograde directions, suggesting that the IFT/BBSome entry into the cilia and exit from the cilia are impacted. Furthermore, anterograde IFT in the middle segment travels at increased speed in wdr-31;rpi-2;elmd-1. Remarkably, a non-ciliary protein leaks into the cilia of wdr-31;rpi-2;elmd-1, possibly because of IFT de-fects. This work reveals WDR31-RP-2-ELMD-1 as IFT and BBSome trafficking regulators.