M.S. Thesis

M.S. Thesis

Talya Tümer Sivri, A Data-Centric Unsupervised 3D Mesh Segmentation Method

This thesis solves the 3D mesh segmentation problem from a different perspective. This perspective contains a data-centric approach with unsupervised learning. This means that 3D mesh segmentation mapping plays a key role in terms of a data-centric approach. We provide a mapping architecture using the node2vec model that also solves the curse of dimension and transforms 3D mesh data into an embedding vector. Segmentation was obtained using the K-Means clustering algorithm using this embedding vector. Additionally, we provide a new strategy that evaluates the graph embedding vector and a new inertia method calculated on 3D mesh data, geodesic inertia.

Date: 02.12.2022 / 14:00 Place: Computer Engineering - A105


Ömer Faruk Yazar, The Importance of Reanalysis and Resequencing in Unsolved Rare Disease Cases with Interlab Database Collaborations

Genomic sequencing technologies opened a new era for genetic disorder diagnostics. Currently, in over 80% of the cases, the genetic etiology of the diseases can be determined by identifying the causative variations. In this study, we have utilized two next-generation sequencing technologies, Ion-Torrent and Illumina, for a rare disease family with two siblings sharing similar symptoms. In addition to comparing technologies, different assemblies of human reference assemblies are analyzed and the benefits of all are discussed for revealing the variants of unsolved rare disease cases. 

Date: 29.11.2022 / 12:30 Place: A108


Elif Esmer, An Experimental Investigation of Gaze Behavior Modeling in Virtual Reality

The main purpose of this thesis is to investigate the potential effects of the gaze bahavior modeling. To that end, experiments were conducted with human participants in the form of a mock-up job interview setting, where a robot avatar programmed with pre-planned gaze behavior in reference to the model was in charge as the interviewer. The eye-tracking data of the participants were collected and analyzed. The eye-tracking results were compared with traditional Human-Robot Interaction survey data along with the TF-IDF (Term Frequency-Inverse Document Frequency) analysis of the post-experimental oral evaluations.

Date: 29.11.2022 / 16:00 Place: B116


Ekinsu Özkazanç, The Semantics of the Nominalizer –(y)Iş: Dimensions of Factivity and Manner

In an attempt to understand the semantics of –(y)Iş better, this thesis aims to systematically distinguish imperfect -(y)Iş nominals from perfect -(y)Iş nominals, manner-denoting -(y)Iş nominals from eventuality-denoting ones, identify the types of eventuality denoted, and identify the factivity status of eventuality-denoting -(y)Iş nominals. We suggested sets of tests for each category, and we applied these tests to a sample set of data to demonstrate that they can be used to reliably and accurately make these distinctions.

Date: 17.11.2022 / 11:00 Place: A212


Yasemin Kuranel, Technical Debt Specification and Categorization for Software as a Service Applications

An outcome of taking poor decisions or choosing easier solutions for faster code delivery is technical debt. There is a gap in the field to assist managing technical debt for software development companies that work with software as a service applications. This thesis aims to specify and categorize the technical debt present in organizations using software as a service applications, by studying the processes for categorizing technical debt, and specifying the causes and issues arising due to this debt.

Date: 28.11.2022 / 09:30 Place: A212


Tuğçe Gölgeli, A Case Study on The Effect of Route Characteristics on Decision Making in the Sport of Orienteering

When choosing a route in orienteering, it is important to combine physical endurance with mental processes and the ability to adapt to the environment and optimize them correctly. In this study, the components affecting route selection were investigated. For this purpose, the data obtained from athletes through GPS containing watches were examined with quantitative and qualitative research methods. Then, a model based on spatial data was created to find the shortest paths and to compare the compatibility with the behaviors of athletes, and the relation of route selection decisions with some specified cognitive paradigms was questioned.

Date: 30.01.2020 / 10:00 Place: A-108


Gökçe Abay, Biological Data Integration and Relation Prediction by Matrix Factorization

In this study, we propose to integrate large-scale gene/protein annotation data by using non-negative matrix factorization (NMF). Using NMF, the ultimate aim here is to predict the unknown binary relationships between these biological entities; and to represent these entities (i.e., proteins, functions and disease entries) as informative and non-redundant quantitative feature vectors (using the low-rank feature matrices generated by the factorization process), which can be used in diverse data mining and machine learning tasks in the future, such as the automated annotations of proteins or the construction of biological knowledge graphs.

Date: 30.01.2020 / 15:30 Place: A-212


Fatma Cankara, Prediction of the Effects of Single Amino Acid Variations on Protein Functionality with Structural and Annotation Centric Modeling

Studies showed that single nucleotide variations that alter the protein sequence, structure and function are associated with many diseases in humans. However, the current rate of manually annotating reported nsSNPs cannot catch up with the rate of producing new sequencing data. To aid this process, automated computational approaches are being developed and applied on the unknown data. In this study, we propose a new methodology to collect and organize the information related to the effects of nsSNPs at the amino acid sequence level from various biological databases and to utilize this information in a supervised machine-learning based system to predict the function disrupting capacities of mutations with unknown consequences.

Date: 30.01.2020 / 14:00 Place: A-212


Rumeysa Fayetörbay, Network-Based Discovery of Molecular Targeted Agent Treatments in Hepatocellular Carcinoma

Sorafenib is one of FDA approved targeted agents in HCC treatment. PI3K/AKT pathway is altered in 50% of hepatoma, hence understanding how Sorafenib and PI3K/AKT pathway inhibitors act at signalling level is crucial for targeted therapies and to reveal their off-target effects. In this work, we use gene expression profiles of HCC cells treated with seven different drugs/inhibitors and combination. Our aim is to reveal the important targets and modulators in a drug treatment by inferring the dysregulation of Interactome. In other words, we search for the mechanism of action of drugs in a network context beyond gene list.

Date: 13.01.2020 / 14:00 Place: A-108



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