A new method has been developed to study allosteric protein regulation, which is important for understanding how proteins function. The method represents proteins as networks and identifies allosteric pathways, or sites where molecules bind and regulate protein activity, in order to predict the presence and location of allosteric regions. This could potentially aid in the development of targeted therapies.

Date: 28.07.2023 / 10:00 Place: A-212

In Türkiye, Social Security Institution is the primary healthcare insurer. Turkish citizens are registered under General Medicare Insurance coverage. In 2003, Ministry of Health (MoH) has initiated the “Health Transformation Program”, and implemented the interoperable health data exchange standards. The MoH is focusing on collecting medical data in a coded, structured, and electronic format, generated at all healthcare providers. Contrarily, genetic test results are exchanged in narrative, unstructured form among governmental and private health care providers. In this dissertation, we lay out the bottlenecks and put forward a conceptual model for meaningful genomic data exchange for Turkish Electronic Health Records.

Date: 24.07.2023 / 17:00 Place: A-108

Building on an extensive review of the psycholinguistics literature and Turkish Derivational Morphology (DM), we propose a novel structure for representing DM in three hierarchical layers: segmentation, lexical selection and derivation. This proposal involves laying a conventionalized structure over the traditional morphological structure of DM. We develop a computational model of morphology processing based on this structure using Bayesian Belief Networks (BBN). We present an algorithmic implementation for this model that learns and accurately represents new lexical items, recognizes affixes and tracks the salience of each item probabilistically. We carry out trials on this model with realistic observation lists and observe that model predictions are in line with the findings in studies in psycholinguistics.

Date: 21.07.2023 / 11:30 Place: A-212

This thesis introduces a novel loss function, the Class Distance Weighted Cross Entropy (CDW-CE) loss, for automated severity assessment of Ulcerative colitis (UC) using Convolutional Neural Networks (CNN) on endoscopic images. CDW-CE considers ordinal relationships between classes and enhances prediction accuracy, outperforming other loss functions across different metrics and architectures. It also improves class activation maps' precision, aiding explanation of model predictions. The approach's broad applicability is confirmed by successful testing on a diabetic retinopathy dataset. The study also created the largest public UC image dataset.

Date: 17.07.2023 / 12:30 Place: A-212

The study introduces a new method to classify Hepatocellular Carcinoma (HCC) using a hyperspectral imaging system (HSI) combined with a light microscope. This method leverages 3D convolutions in Convolutional Neural Networks (CNNs) to train a robust classifier, capturing unique spectral and spatial features automatically. The approach also addresses class imbalance in the dataset by employing a focal loss function, preventing overfitting. The results show that hyperspectral data surpasses RGB data in liver cancer tissue classification, and enhanced spectral resolution improves accuracy, highlighting the importance of both spectral and spatial features for effective cancer tissue classification.

Date: 19.06.2023 / 15:45 Place: B-116

Hepatocellular carcinoma (HCC) is a type of primary liver cancer that is highly lethal and needs better treatment options. The thesis identifies several drugs and drug combinations that show promise in targeting drug-resistant HCC cells and cancer stem cells based on in silico modeling and in vivo experiments. It found that a combination of Sunitinib and Chloroquine Phosphate is synergistically cytotoxic on HCC cells, while novel isoxazole-piperazine compounds also show bioactivities against HCC cells and cancer stem cells. The study presents significant findings that highlight the potential of repurposed drugs and novel compounds as drug candidates for HCC.

Date: 24.04.2023 / 10:30 Place: A-212

In this study, by using facial images and EMG recordings, a novel assessment method based on computation will be developed. The method to be developed will be quantified, repeatable, and easy to run autonomously on a regular computer or device.

Date: 07.04.2023 / 11:00 Place: A-212

In this thesis study, we focus on the construction of an effective network architecture, for which we propose an architecture generation framework and show how it can be used to create an effective terrain classification model. Additionally, we also observe that land cover training data sets on HSI and LiDAR tend to come short in providing training examples with shadow effects. To address this limitation, we additionally propose a generative adversarial network(GAN) driven statistical data augmentation technique that generates synthetic training examples and show its effectiveness in our experimental results.

Date: 26.01.2023 / 14:30 Place: A-108

This thesis demonstrates research towards innovating a novel framework that (i) could be trained by supervised learning of discourse parsing from annotated and parsed relations, (ii) makes it easy to build end-to-end shallow discourse parsing system, (iii) forms a discourse relation detector benchmark for low resourced languages. Our modern neural network approach, integrated with contextualized text embedding, produced by pretrained language models, accomplished two sub-tasks of shallow discourse parsing, namely, identification of discourse relation realization types and the sense classification of explicit and implicit relations. The effect of multilingual data aggregation on the classification of discourse relation type through Cross-lingual Transfer Learning experiments is researched too.

Date: 25.01.2023 / 10:00 Place: A-108

Building on an extensive review of the psycholinguistics literature and Turkish Derivational Morphology (DM), we propose a novel structure for representing DM in three hierarchical layers: segmentation, lexical selection and derivation. This proposal involves laying a conventionalized structure over the traditional morphological structure of DM. We develop a computational model of morphology processing based on this structure using Bayesian Belief Networks (BBN). We present an algorithmic implementation for this model that learns and accurately represents new lexical items, recognizes affixes and tracks the salience of each item probabilistically. We carry out trials on this model with realistic observation lists and observe that model predictions are in line with the findings in studies in psycholinguistics.

Date: 25.01.2023 / 12:00 Place: A-108