State-of-the-art omics technologies use network-based contextualization methods to give molecular information about different biological contexts, like disease states, patients, and drug changes. In the beginning, this thesis identified challenging issues such as missing points in contextualization, hidden knowledge in omics datasets, bias in reference networks, and noisy interactions with highly connected nodes or hubs. Subsequently, to address these challenges, we developed pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omics data integratioN). Also, a novel tool, pyPARAGON, contextualized patient datasets by inferring patient-specific networks and complex diseases by constructing disease models, namely breast cancer and autism spectrum disorders.

Date: 22.01.2024 / 14:00 Place: A-108

Despite impressive achievements made by machine learning algorithms (especially in deep learning), they are easily tricked by modified input data. Adversarial attacks target machine learning models severely. Adversarial training is an effective method against adversarial attacks, but it is not suitable for network domains due to network flow characteristics. In this thesis, the autoencoder's reconstruction error is used for detecting adversarial attacks. The IDS model, RAIDS is proposed and achieves respectful results against adversarial attacks.

Date: 17.01.2024 / 14:00 Place: A-108

Global climate change induces lake level fluctuations, impacted by evolving meteorological factors and water use. Input or output changes swiftly affect the water balance equation. This study explores predictive models for climatic and hydrologic variables, assessing their correlations with lake water level and water quality. Using diverse algorithms—Naive Method, ANN, and RNN—LSTM excels in accuracy by RMSE. Comparisons with the Naïve Method confirm ANN and RNN predictive prowess, especially with extended horizons. Correlations with temperature and evaporation highlight lake water quality impacts. Models and metrics construct a decision support tool for water managers.

Date: 19.12.2023 / 13:30 Place: A-212

We developed an efficient and scalable method for identification of signature sequences that can handle thousands of whole genomes for organisms with high mutation rates and genetic diversity. Thermodynamics is the main driving force in our method, which is tested on three highly divergent viruses. The oligonucleotides found can identify 99.9% of 1657 HCV genomes, 99.7% of 11838 HIV genomes, and 95.4% of 4016 Dengue genomes. We also show subspecies identification on genotypes 1-6 of HCV and genotypes 1-4 of the Dengue virus with >99.5% true positive and <0.05% false positive rate. None of the state-of-the-art methods achieve this performance.

Date: 11.09.2023 / 17:00 Place: A-212

Most of the variants in the genome are at the non-coding region. While variations in the coding region effect the protein, variations in non-coding region effect the regulatory mechanism. Therefore, observation of non-coding variations may ensure to identify variations that effect gene expression. eQTL is a popular method used for the purpose to determine the SNPs that effect the gene expression. We have implemented a python based, easy-to-use tool to understand the relationship between the somatic SNPs and gene expression based on eQTL analysis.

Date: 11.09.2023 / 14:00 Place: A-212

In this study, changes in neural activation during joint action were investigated with hyperscanning, using functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) as participants performed the same task individually and then collaborated as a dyad. 62 participants were tested in dyads with a dual n-back task. Results are in support of the social facilitation model, and neural measures including hyperscanning were successful to differentiate between individual and social task settings. Overall, this study is one of the first studies to conduct EEG and fNIRS-hyperscanning during a complex collaboration task that brought together temporal constraints and elements of visuospatial reasoning.

Date: 05.09.2023 / 11:00 Place: A-212

This study aims to provide a bibliometric map of Functional Near Infrared Spectroscopy (fNIRS), a growing research area worldwide, in terms of interdisciplinary and institutional collaboration. Interdisciplinary and institutional collaborations are increasingly encouraged by universities as they have a high academic citation impact. In this study, the number of articles, number of citations, CNCI and IREW percentage in the field of fNIRS from 1980 to 2020 were analysed. Interdisciplinary and university & country collaborations that can increase these academic indicators are analysed from a bibliometric perspective.

Date: 11.09.2023 / 10:30 Place: -

This PhD thesis delves into the impact of selective directional auditory attention on eye movements and pupil responses. Concentrating on single-ear stimuli is shown to enhance attentiveness and task performance. The research explores effects of multi-talker speech interference, age, gender, and audio direction on eye movements and pupil dilation. Notably, interference influences pupils, prompting gaze shifts toward sound sources and larger pupils during competing speakers. This underscores the study's significance. These findings highlight the value of eye movement patterns and pupillometry in understanding auditory attention nuances. The implications are profound, benefiting hearing aid technology, communication for autism spectrum disorder and hearing-impaired individuals. The study also uncovers links between speech quality, word familiarity, and emotional connotations on pupil responses, contributing to a comprehensive comprehension of auditory processes.

Date: 06.09.2023 / 10:00 Place: A-212

This study aims for detection and DOA estimation of gunshot sound onboard a drone, despite the excessive ego-noise. Array Correlation Map concept is introduced for improved detection through unanimity among sensors of an array. Also, adaptive auto-tuning to advantegous CWT scales brings adaptive denoising for transient events of varying frequency. Although studied specifically for the processing of gunshot sounds on drones, the novelties this study offers is expected to generalize to other array processing applications. Results reveal signal-to-noise-ratio enhancement, successful muzzle and shock wave signal detection, and DOA estimation performance improvement.

Date: 04.09.2023 / 10:00 Place: A-212

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