Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Development of Visual Analysis Interfaces for Large Biological Data and Characterization of Immunomodulatory Noncoding Rna Networks Cancer(01. Izmir Institute of Technology, 2023) Kuş, Muhammet Emre; Ekiz, Hüseyin Atakan; Ekiz, Hüseyin AtakanThese days we are collecting data in higher and higher dimensions, processing it, and developing tools that have strong descriptive and predictive powers. Especially in the field of cancer, the processing of data collected from patients has substantial potential in terms of discovering new biomarkers, developing personalized treatment methods, and better prognosticators. However, there are significant difficulties in utilizing and analyzing high-dimensional data. A good level of coding skills is required to bring the data together and apply different analysis methods. With the visual interfaces created in this study, we offer the opportunity to examine and analyze the high-dimensional data of thousands of cancer patients, which are open to the public through The Cancer Genome Atlas initiative, especially for bench scientists who has no prior coding expertise. The Cancer Genome Explorer, shortly TCGEx, is a robust bioinformatic tool that we developed to facilitate high-throughput cancer data analysis through several sophisticated algorithms. With special features like subset-specific analysis and comparative analysis by using multiple cancer data, TCGEx can contribute to the literature by accelerating the studies, especially in hypothesis-driven research. This study also describes a use-case scenario that demonstrates how hypothesis-driven research can be performed using TCGExplorer for melanoma. In melanoma, elucidating the interactions between the tumor and the immune system at the miRNA level is crucial for developing new therapeutics. In this study, we characterize the properties of potential therapeutic targets that act on tumor and immune cells, which we have identified using various statistical analysis methods including machine learning, dimensionality reduction, and survival modeling using the TCGEx portal.Master Thesis Deep Learning Based Real-Time Sequential Facial Expression Analysisusing Geometric Features(01. Izmir Institute of Technology, 2023) Köksal, Talha Enes; Gümüş, AbdurrahmanIn this thesis, macro and micro facial expression sequences from various datasets are trained using neural networks to classify them in one of the basic emotions. In macro expression experiments, for each frame of the sequences facial landmarks are extracted using MediaPipe FaceMesh solution and geometric features using both spatial and temporal information based on these landmarks are created. To classify the features, ConvLSTM2D followed by multilayer perceptron blocks are used. In order to achieve real time classification performance, all algorithms are implemented compatible to run on GPU. The proposed method for macro expressions is tested with CK+, Oulu-CASIA VIS, Oulu-CASIA NIR and MMI datasets. In micro expression experiments, apart from geometric features also blendshape features provided by MediaPipe are used. In order to improve classification performance, Phase-Based Video Motion Processing technique is used to magnify subtle facial movements of micro expressions. Experiments are conducted separately on same classification layers that consist of ConvLSTM1D followed by multilayer perceptron blocks. The proposed method for micro expressions is tested with SAMM and CASME II datasets. The datasets utilized in this study were accessed upon signing corresponding license agreements. Each dataset is specifically designated for academic purposes and is made available under these agreements. Only data from subjects who provided consent for their information to be used in publications was included in the thesis. The license agreements for each dataset can be found in the appendices section.