PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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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: 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: 26Citation - Scopus: 31miRcorrNet: 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 - WoS: 24Citation - Scopus: 26Wireless Measurement of Elastic and Plastic Deformation by a Metamaterial-Based Sensor(MDPI, 2014-10-20) Ozbey, Burak; Demir, Hilmi Volkan; Kurc, Ozgur; Erturk, Vakur B.; Altintas, AyhanWe report remote strain and displacement measurement during elastic and plastic deformation using a metamaterial-based wireless and passive sensor. The sensor is made of a comb-like nested split ring resonator (NSRR) probe operating in the near-field of an antenna, which functions as both the transmitter and the receiver. The NSRR probe is fixed on a standard steel reinforcing bar (rebar), and its frequency response is monitored telemetrically by a network analyzer connected to the antenna across the whole stress-strain curve. This wireless measurement includes both the elastic and plastic region deformation together for the first time, where wired technologies, like strain gauges, typically fail to capture. The experiments are further repeated in the presence of a concrete block between the antenna and the probe, and it is shown that the sensing system is capable of functioning through the concrete. The comparison of the wireless sensor measurement with those undertaken using strain gauges and extensometers reveals that the sensor is able to measure both the average strain and the relative displacement on the rebar as a result of the applied force in a considerably accurate way. The performance of the sensor is tested for different types of misalignments that can possibly occur due to the acting force. These results indicate that the metamaterial-based sensor holds great promise for its accurate, robust and wireless measurement of the elastic and plastic deformation of a rebar, providing beneficial information for remote structural health monitoring and post-earthquake damage assessment.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.Article Citation - WoS: 15Citation - Scopus: 14Understanding Plasmon Coupling in Nanoparticle Dimers Using Molecular Orbitals and Configuration Interaction(Royal Soc Chemistry, 2019) Alkan, Fahri; Aikens, Christine M.We perform a theoretical investigation of the electronic structure and optical properties of atomic nanowire and nanorod dimers using DFT and TDDFT. In both systems at separation distances larger than 0.75 nm, optical spectra show a single feature that resembles the bonding dipole plasmon (BDP) mode. A configuration interaction (CI) analysis shows that the BDP mode arises from constructive coupling of transitions, whereas the destructive coupling does not produce significant oscillator strength for such separation distances. At shorter separation distances, both constructive and destructive coupling produce oscillator strength due to wave-function overlap, which results in multiple features in the calculated spectra. Our analysis shows that a charge-transfer plasmon (CTP) mode arises from destructive coupling of transitions, whereas the BDP results from constructive coupling of the same transitions at shorter separation distances. Furthermore, the coupling elements between these transitions are shown to depend heavily on the amount of exact Hartree-Fock exchange (HFX) in the functional, which affects the splitting of CTP and BDP modes. With 50% HFX or more, the CTP and BDP modes mainly merge into a single feature in the spectra. These findings suggest that the effects of exact exchange must be assessed during the prediction of CTP modes in plasmonic systems.Article Citation - WoS: 13Citation - Scopus: 13Tuning the Shades of Red Emission in InP/ZnSe Nanocrystals With Narrow Full Width for Fabrication of Light-Emitting Diodes(Amer Chemical Soc, 2023-10-13) Soheyli, Ehsan; Bicer, Aysenur; Ozel, Sultan Suleyman; Tiras, Kevser Sahin; Mutlugun, Evren; Sahin Tiras, KevserWhile Cd-based luminescent nanocrystals (NCs) are the most mature NCs for fabricating efficient red light-emitting diodes (LEDs), their toxicity related limitation is inevitable, making it necessary to find a promising alternative. From this point of view, multishell-coated, red-emissive InP-based NCs are excellent luminescent nanomaterials for use as an emissive layer in electroluminescent (EL) devices. However, due to the presence of oxidation states, they suffer from a wide emission spectrum, which limits their performance. This study uses tris-(dimethyl-amino)-phosphine (3DMA-P) as a low-cost aminophosphine precursor and a double HF treatment to suggest an upscaled, cost-effective, and one-pot hot-injection synthesis of purely red-emissive InP-based NCs. The InP core structures were coated with thick layers of ZnSe and ZnS shells to prevent charge delocalization and to create a narrow size distribution. The purified NCs showed an intense emission signal as narrow as 43 nm across the entire red wavelength range (626-670 nm) with an emission quantum efficiency of 74% at 632 nm. The purified samples also showed an emission quantum efficiency of 60% for far-red wavelengths of 670 nm with a narrow full width of 50 nm. The samples showed a relatively long average emission lifetime of 50-70 ns with a biexponential decay profile. To demonstrate the practical ability of the prepared NCs in optoelectronics, we fabricated a red-emissive InP-based LEDs. The best-performing device showed an external quantum efficiency (EQE) of 1.16%, a luminance of 1039 cd m(-2), and a current efficiency of 0.88 cd A(-1).Article Citation - WoS: 4Citation - Scopus: 4Transparent Films Made of Highly Scattering Particles(Amer Chemical Soc, 2020-01-13) Erdem, Talha; Yang, Lan; Xu, Peicheng; Altintas, Yemliha; O'Neil, Thomas; Caciagli, Alessio; Eiser, ErikaToday, colloids are widely employed in various products from creams and coatings to electronics. The ability to control their chemical, optical, or electronic features by controlling their size and shape explains why these materials are so widely preferred. Nevertheless, altering some of these properties may also lead to some undesired side effects, one of which is an increase in optical scattering upon concentration. Here, we address this strong scattering issue in films made of binary colloidal suspensions. In particular, we focus on raspberry-type polymeric particles made of a spherical polystyrene core decorated by small hemispherical domains of acrylate with an overall positive charge, which display an unusual stability against aggregation in aqueous solutions. Their solid films display a brilliant red color due to Bragg scattering but appear completely white on account of strong scattering otherwise. To suppress the scattering and induce transparency, we prepared films by hybridizing them with oppositely charged PS particles with a size similar to that of the bumps on the raspberries. We report that the smaller PS particles prevent raspberry particle aggregation in solid films and suppress scattering by decreasing the spatial variation of the refractive index inside the film. We believe that the results presented here provide a simple strategy to suppress strong scattering of larger particles to be used in optical coatings.Article Citation - WoS: 11Citation - Scopus: 12Towards Analysis and Optimization for Contact Zone Temperature Changes and Specific Wear Rate of Metal Matrix Composite Materials Produced From Recycled Waste(MDPI, 2021-09-08) Gunes, Aydin; Salur, Emin; Aslan, Abdullah; Kuntoglu, Mustafa; Giasin, Khaled; Pimenov, Danil Yurievich; Sahin, Omer SinanTribological properties are important to evaluate the in-service conditions of machine elements, especially those which work as tandem parts. Considering their wide range of application areas, metal matrix composites (MMCs) serve as one of the most significant materials equipped with desired mechanical properties such as strength, density, and lightness according to the place of use. Therefore, it is crucial to determine the wear performance of these materials to obtain a longer life and to overcome the possible structural problems which emerge during the production process. In this paper, extensive discussion and evaluation of the tribological performance of newly produced spheroidal graphite cast iron-reinforced (GGG-40) tin bronze (CuSn10) MMCs, including optimization, statistical, graphical, and microstructural analysis for contact zone temperature and specific wear rate, are presented. For this purpose, two levels of production temperature (400 and 450 degrees C), three levels of pressure (480, 640, and 820 MPa), and seven different samples reinforced by several ingredients (from 0 to 40 wt% GGG-40, pure CuSn10, and GGG-40) were investigated. According to the obtained statistical results, the reinforcement ratio is remarkably more effective on contact zone temperature and specific wear rate than temperature and pressure. A pure CuSn10 sample is the most suitable option for contact zone temperature, while pure GGG-40 seems the most suitable material for specific wear rates according to the optimization results. These results reveal the importance of reinforcement for better mechanical properties and tribological performance in measuring the capability of MMCs.Article Topological Feature Generation for Link Prediction in Biological Networks(PeerJ Inc, 2023-05-09) Temiz, Mustafa; Bakir-Gungor, Burcu; Sahan, Pinar Guner; Coskun, Mustafa; Güner Şahan, PınarGraph or network embedding is a powerful method for extracting missing or potential information from interactions between nodes in biological networks. Graph embedding methods learn representations of nodes and interactions in a graph with low-dimensional vectors, which facilitates research to predict potential interactions in networks. However, most graph embedding methods suffer from high computational costs in the form of high computational complexity of the embedding methods and learning times of the classifier, as well as the high dimensionality of complex biological networks. To address these challenges, in this study, we use the Chopper algorithm as an alternative approach to graph embedding, which accelerates the iterative processes and thus reduces the running time of the iterative algorithms for three different (nervous system, blood, heart) undirected protein-protein interaction (PPI) networks. Due to the high dimensionality of the matrix obtained after the embedding process, the data are transformed into a smaller representation by applying feature regularization techniques. We evaluated the performance of the proposed method by comparing it with state-of-the-art methods. Extensive experiments demonstrate that the proposed approach reduces the learning time of the classifier and performs better in link prediction. We have also shown that the proposed embedding method is faster than state-of-the-art methods on three different PPI datasets.