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Informatik 5
Information Systems
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Dr. Oya Deniz Beyan - Theses


Bachelor
Developing a Data Annotation Tool for Scientific Data Management - Open
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan
Semantic technologies and RDF data representation can improve the reusability of scientific data and enable scientist to reproduce the experiments. However there is no tool to support researcher for making their data semantically interpretable by computers. Aim of the thesis is to understand the benefits of semantic web technologies for reproducible research and develop a tool which can convert experimental data to RDF by annotating with selected data models.
Bachelor
Classification of Cancer with methylation aware motifs - Running
DNA methylation data has become a popular choice as features for cancer classification tasks. However, a big problem arising here is its high dimensionality in combination with the usually small amount of available samples. Therefore, we created lower-dimensional meta-features that take the effects of the altered DNA methylation pattern on the binding affinity of human Transcription Factors into account. Their performance is evaluated in this thesis.
Master
FAIR Identifier Registry for Distributed Systems - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan
The active increase of digital data, as well as the generation of new varieties of digital content, has established several possibilities and challenges in the management of the big amount of data. Traditionally, organisations have relied on Uniform Resource Locator (URL) hyperlinks to provide involved parties with access to their digitised content via the Internet. Nevertheless, over time, more of these hyperlinks become invalid. The concept of persistent identification has been developed to solve this issue. Instead of addressing data directly through its actual locator, Persistent Identifiers (PIDs) permit data retrieval by globally unique and permanent identifiers. PIDs include metadata and resolving URLs that point to the original location of data collections. As the target URLs are changing, PIDs require constant maintenance. Existing PID systems has enabled long-term stable and unambiguous references. They are already in rapid use, supporting distributed approaches at varying levels. In most of the cases, PIDs are indexed and explored by a central system. Besides infrastructural questions, the discoverability of PIDs mainly depends on their metadata classifications, which express what a given PID represents. The aim of this thesis is to explore the creation of a meta-standard for the specification of domain-specific PID registries which are exposed by an application programming interface (API), while standard and API both respect Findable, Accessible, Interoperable and Reusable (FAIR) principles.
Bachelor
Towards Automating Graph Data Cleansing Using Shapes - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan, Ph.D. Heiner Oberkampf
Data curation is a labour intensive process transforming often completely unstructured data into structured data. This work proposes an approach to improve the quality of semi structured data efficiently and resolve conflicts with minimal effort. This is achieved by inferring a data model or improving a given one, by finding inconsistencies within the data, and by suggesting possible data edits using different machine learning and data mining techniques. Effort is further reduced by detecting conflicts that were caused by other conflicts using root cause analysis.
Master
Unsupervised Anomaly Detection in Medical Time Series Data - Finished
Completed by Brockhoff, Tobias in 2019; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan
Master
Deep Learning-based Knee Osteoarthritis Diagnosis from Radiographs and Magnetic Resonance Images - Finished
Completed by Jiao, Jiao in 2019; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan, Dr. Michael Cochez, Md. Rezaul Karim, M.Sc.
In this thesis, the student investigates the use of deep learning techniques (especially computer vision) to perform diagnosis of osteoarthritis. The input to the system are both radiographs (X-RAY) and magnetic resonance images (MRI).
Master
Master
Developing a machine-actionable process model for ethical approval workflows - Finished
Completed by Schwaiger, Verena in 2019; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan
Bachelor
Master
Patterns zur Integration regel- und prozessbasierter Modellkomponenten bei der Operationalisierung klinischer Leitlinien - Finished
Completed by Lammers, Florian in 2019; Supervised by Prof. Dr. Stefan Decker, Dr. rer. nat. Cord Spreckelsen; Advisor(s): Dr. Oya Deniz Beyan
Master
Semantic Anomaly Detection in Medical Time Series - Finished
Completed by Sven Festag in 2019; Supervised by Prof. Dr. Stefan Decker, Dr. rer. nat. Cord Spreckelsen; Advisor(s): Dr. Oya Deniz Beyan
Master
Using Deep Neural Networks and Copy Number Variations for Cancer Detection - Finished
Completed by Rahman, Md Ashiqur in 2018; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan, Md. Rezaul Karim, M.Sc.
Recently copy number variations got lot of attention due to their association with complex diseases like cancer. Finding their association with cancer is an active research eld. We propose a method to identify copy number variations which might be associated with a cancer by comparing copy numbers across 14 major cancer types. Selected CNV regions were used as features for cancer detection using deep belief networks, a deep learning technique. To our knowledge this is the rst study involving that many cancers and highest number of control samples for validation.
Master
Analysis of Breast Cancer Genomic Data with Multimodal Deep Belief Network - Finished
Completed by Wicaksono, Galih Gilang in 2018; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan, Md. Rezaul Karim, M.Sc.
The objective of this thesis is to analyse breast cancer genomic data with the potential of predicting breast cancer genomic biomarker. Specifically, these analysis will include classification and regression of breast cancer patients based on their genetic information.