Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
Search Results
Doctoral Thesis FUNCTIONALIZED LOW LUMO [1]BENZOTHIENO[3,2-B][1]BENZOTHIOPHENE (BTBT)-BASED MOLECULAR SEMICONDUCTORS FOR ORGANIC FIELD EFFECT TRANSISTORS(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2021) Özdemir, Resul; Usta, HakanDAcTTs have provided an excellent π-framework for the development of high mobility p-type molecular semiconductors in the past decade. However, n-type DAcTTs are rare and their electron transporting characteristics remain largely unexplored. In the second chapter of this thesis, the first example of an n-type BTBT-based semiconductor, D(PhFCO)-BTBT, has been realized via a two-step transition metal-free process without using chromatographic purification. The corresponding TC/BG-OFET devices demonstrated μe (max) = ~0.6 cm2/Vs and Ion/Ioff ratio = 107-108. The large band-gap BTBT π-core is a promising candidate for high mobility n-type organic semiconductors and, combination of very large intrinsic charge transport capabilities and optical transparency, may open a new perspective for next-generation (opto)electronics. In the third chapter of this thesis, a series of BTBT-based small molecules, D(C7CO)-BTBT, C7CO-BTBT-CC(CN)2C7, and D(C7CC(CN)2)-BTBT, have been developed in “S-F-BTBT-F-S (F/S: functional group/substituent)” molecular architecture. Combining with D(PhFCO)-BTBT, a molecular library with systematically varied chemical structures has been studied herein for the first time for low LUMO DAcTTs, and key relationships have been elucidated. The molecular engineering perspectives presented in this thesis may give unique insights into the design of novel electron transporting thienoacenes for unconventional optoelectronics.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: .Article Citation - WoS: 27Citation - Scopus: 32miRcorrNet: 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.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: 13Citation - Scopus: 13Why Do Muse Stem Cells Present an Enduring Stress Capacity? Hints From a Comparative Proteome Analysis(MDPI, 2021-02-19) Acar, Mustafa B.; Aprile, Domenico; Ayaz-Guner, Serife; Guner, Huseyin; Tez, Coskun; Di Bernardo, Giovanni; Galderisi, UmbertoMuse cells are adult stem cells that are present in the stroma of several organs and possess an enduring capacity to cope with endogenous and exogenous genotoxic stress. In cell therapy, the peculiar biological properties of Muse cells render them a possible natural alternative to mesenchymal stromal cells (MSCs) or to in vitro-generated pluripotent stem cells (iPSCs). Indeed, some studies have proved that Muse cells can survive in adverse microenvironments, such as those present in damaged/injured tissues. We performed an evaluation of Muse cells' proteome under basic conditions and followed oxidative stress treatment in order to identify ontologies, pathways, and networks that can be related to their enduring stress capacity. We executed the same analysis on iPSCs and MSCs, as a comparison. The Muse cells are enriched in several ontologies and pathways, such as endosomal vacuolar trafficking related to stress response, ubiquitin and proteasome degradation, and reactive oxygen scavenging. In Muse cells, the protein-protein interacting network has two key nodes with a high connectivity degree and betweenness: NFKB and CRKL. The protein NFKB is an almost-ubiquitous transcription factor related to many biological processes and can also have a role in protecting cells from apoptosis during exposure to a variety of stressors. CRKL is an adaptor protein and constitutes an integral part of the stress-activated protein kinase (SAPK) pathway. The identified pathways and networks are all involved in the quality control of cell components and may explain the stress resistance of Muse cells.