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RNA Etkileşimlerinin İn Silico Analizi
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Orhan, Mehmet Emin; Demirci, Müşerref Duygu Saçar
Many supervised machine learning models have been developed for the classification and identification of non-coding RNA (ncRNA) sequences. These models play a significant role in the diagnosis and treatment of various diseases. During such analyses, positive learning datasets typically consist of known ncRNA examples, some of which may even be confirmed with strong experimental evidence. However, there is no database of validated negative sequences for ncRNA classes or standardized methodologies for generating high quality negative samples. To overcome this challenge, a new method for generating negative data called the NeRNA (Negative RNA) method has been developed in this study. NeRNA generates negative sequences using known ncRNA sequences and their octal representations, similar with frame shift mutations found in biology but without base deletions or insertions. In this thesis, the NeRNA method was tested separately with four different ncRNA datasets, including microRNA (miRNA), transfer RNA (tRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA). Additionally, a species-specific case study was conducted to demonstrate and compare the performance of the study's miRNA predictions. The results of 1000-fold cross-validation on machine learning algorithms such as Decision Trees, Naive Bayes, Random Forest classifiers, and deep learning algorithms like Multilayer Perceptrons, Convolutional Neural Networks, and Simple Feedforward Neural Networks showed that models developed using datasets generated by NeRNA exhibited significantly high prediction performance. NeRNA has been published as an easy-to-use, updatable, and modifiable KNIME workflow, along with example datasets and required extensions that can be downloaded and utilized. NeRNA is designed specifically as a powerful tool for RNA sequence data analysis.
Makine Öğrenimi Algoritmalarını Kullanarak Ağ Trafiğini Analiz Etme ve Ağ Tehditlerini Tespit Etme
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Küçükkoç, Abdurrahman; Aydın, Zafer
As information technologies progress, the possibilities of access to information increase and therefore it becomes difficult to ensure the security of information. Today, with the use of information systems in all areas of life, network threats have also increased. The increase in individual access to and use of the internet has also brought network threats. In addition, the latest developments in information technologies, developing global communication networks, the internet of things aiming to connect all objects with networks, cloud technologies, the spread of mobile internet and the renewal of devices have brought network threats and uncertainties. Network threats increase the security vulnerabilities in the information and communication systems of individuals and organisations day by day. This situation causes systems to malfunction, economic damage and cyber security to be jeopardised. In order to contribute to individuals, institutions and organisations, our thesis aims to protect information systems against network threats, to ensure data confidentiality, integrity and accessibility, to detect network threats in advance and to take measures against these threats. We believe that by analysing heterogeneous network traffic, which includes most network attacks on the Internet, and using machine learning algorithms, we will reach a result close to reality in the detection of network threats. In line with this result, we will be able to take precautions against network threats in information systems and structures

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