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: 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: 40Citation - Scopus: 49Spatio-Temporal Rich Model-Based Video Steganalysis on Cross Sections of Motion Vector Planes(Institute of Electrical and Electronics Engineers Inc., 2016-07) Taşdemir, K.; Kurugöllü, F.; Sakir Sezer, S.; Sezer, SakirA rich model-based motion vector (MV) steganalysis benefiting from both temporal and spatial correlations of MVs is proposed in this paper. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this paper. First, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring MVs for longer distances. Therefore, temporal MV dependency alongside the spatial dependency is utilized for rigorous MV steganalysis. Second, unlike the filters previously used, which were heuristically designed against a specific MV steganography, a diverse set of many filters, which can capture aberrations introduced by various MV steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in the previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-Temporal range and provides a better cover model. The proposed method is tested against the most prominent MV steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in MV steganalysis field, including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads. © 2016 Elsevier B.V., All rights reserved.