Graduate School of Informatics

We employ the XGBoost algorithm to predict driver non-coding mutations based on multiple engineered features, augmented with features from existing annotation and effect prediction tools. The resulting dataset is passed through a feature selection and engineering pipeline and then trained to predict driver versus passenger non-coding mutations. We also use this model within the architecture of a known driver discovery model from existing literature. We then use non-coding driver mutations found in previous studies and predict their driver-ness using our models. Furthermore, we use Explainable AI methodologies to perform an in-depth analysis of the generated predictions.

Date: 07.06.2024 / 10:00 Place: A-212

This thesis explores attribution mechanisms in Turkish discourse through the adaptation of the Penn Discourse TreeBank (PDTB) framework, resulting in the Turkish Discourse Bank (TDB 1.2). Utilizing insights from lexical control and eventuality specific to Turkish, a custom annotation scheme was developed, facilitating robust data annotation. Analysis shows the predominance of communicative verbs in attribution instances, highlighting novels and news as rich domains for study. Achieving high inter-annotator agreement, this work advances the field by enriching the TDB and laying groundwork for future automated text analysis in Turkish.

Date: 04.09.2024 / 10:00 Place: B-116

This study investigates the association between SNPs and COVID-19 susceptibility in the Turkish population, focusing on patients from Hacettepe University Hospital. Using NGS, we analyzed SNP data from various scientific publications, performing variant calling, linkage analysis, and statistical comparisons with non-Finnish European allele frequencies. Key findings indicate that certain variants have different frequencies compared to the European population, suggesting genetic predispositions affecting disease susceptibility in the Turkish population. Linkage disequilibrium analysis revealed strong correlations between specific genetic loci.

Date: 23.07.2024 / 15:00 Place: A-212

The most interactive field of digital transformation is data science, as it entails a longtime active collaboration among multiple partners. Data scientists seek domain expertise to understand the structure and environment of the data while business users take pains with concepts to exploit analytical solutions. This thesis presents the conceptual design and implementation of CoDS (Collaborative Data Science Framework) as a knowledge management system on which business and data details, modeling procedures, and deployment steps are shared. It mediates and scales ongoing projects, enriches knowledge transfer among stakeholders, facilitates ideation of new products, and supports the onboarding of new developers.

Date: 06.06.2024 / 11:00 Place: II-06

This study examines infrastructure issues and system malfunctions in Cloud Computing systems exacerbated by the COVID-19 pandemic, which acts as a stress test due to increased demand. It is argued that task scheduling algorithms are the main source of these problems. Post-pandemic Cloud Computing task scheduling algorithms were systematically reviewed and analyzed using the Meta-Synthesis method. A global categorization schema for these algorithms was presented, comparing their advantages, disadvantages, applications and vulnerabilities. Current task scheduling approaches and trends in Cloud Computing were analyzed comparatively.

Date: 27.05.2024 / 13:30 Place: B-116

The study aimed to understand the impact of IT governance mechanisms on IT agility. Results indicated that relational IT governance mechanisms significantly enhance IT agility, unlike structures and processes, which were not significant. Relational mechanisms involve top management support, cross-functional training, and a clear IT role within the firm, fostering mutual understanding and integration between IT and business units. While structures and processes are essential for compliance, they may not independently achieve agility. Future research should investigate how these formal mechanisms enable or inhibit agility. Additionally, a hybrid IT governance model may balance centralization and decentralization, offering both efficiency and flexibility.

Date: 22.05.2024 / 13:00 Place: A-212

This thesis aims to make experimental studies on malware family classification using deep learning algorithms. A new dataset called MamMalware which is publicly available and has 450K labeled malware was created within this study. Samples in dataset were translated into gray-scale image files, and the opcode sequences were also extracted. Image files and opcode sequences were used as input. Then 2 and 3 layered Convolutional Neural Networks (CNN) experiments were applied on MamMalware dataset. In addition, experiments using the transfer learning methods with ResNet152 and VGG19 pretrained models were conducted. As a result, the transfer learning models obtained the best results with 94% test accuracy.

Date: 17.05.2024 / 11:00 Place: A-212