Biyomühendislik Ana Bilim Dalı Tez Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/417

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Browsing Biyomühendislik Ana Bilim Dalı Tez Koleksiyonu by Subject "Biostatistics"

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    Master Thesis
    COVID-19 Virüs Pandemisinde Haftalık Döngünün Kökeni ve Sosyo-Ekonomik Faktörlerle İlişkisinin Araştırılması
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Yağmur, İsmail Emre; Ercan, Altan; AGÜ, Fen Bilimleri Enstitüsü, Biyomühendislik Ana Bilim Dalı; 01. Abdullah Gül University
    The Covid-19 virus, which started in China in 2019 and affected the whole world, has caused a global pandemic. Looking at the worldwide data of this pandemic, the number of daily cases appears to have a weekly cycle that is underestimated as an artifact of the number of daily tests administered. In this thesis study, a new model is developed to calculate the daily infection numbers from daily case numbers by using the Weibull distribution and the natural characteristics of the COVID-19 virus. According to the results obtained, it is found that the number of daily cases has a real weekly cycle. It has been determined that the daily infection numbers calculated in this weekly cycle are minimum on weekdays. According to the analysis by the new methos, these weekly minimums are controlled by socio-economic factors such as human development index and annual national income per capita. During the ascending and descending phases of the pandemic, the weekly minimum shifts from Monday to Friday, exposing the presence of two separate environments for the transmission of the virus among people: working and social. Moreover, the data reveal a variable rather than a fixed reproduction number. As a result, the model we developed in this study successfully identifies the socio-economic factors as the effectors of the progression of the pandemic by taking into account the time of infection for the first time in the literature and is expected to guide the future pandemic studies and pandemic, itself.
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    Master Thesis
    RNA Etkileşimlerinin İn Silico Analizi
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Orhan, Mehmet Emin; Demirci, Müşerref Duygu Saçar; AGÜ, Fen Bilimleri Enstitüsü, Biyomühendislik Ana Bilim Dalı; 01. Abdullah Gül University; 04. Yaşam ve Doğa Bilimleri Fakültesi; 04.01. Biyomühendislik
    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.