Graph Neural Networks have become ubiquitous in machine learning research, and their use has also given rise to expectations of what a model can do and how we can understand it. Explainability has become one of the key tools for solving these problems, but explainability often needs to consider domain-specific requirements to be useful. To the best of our knowledge, no domain-specific requirements have been set for the biomedicine domain when working with biomedicine. In this thesis, we seek to understand what standards are needed by the domain, set automatic metrics to meet them, and evaluate these metrics in problems common to biomedicine. Thanks to working on the gene disease association and the proteins classification task, we are able to provide some insights into what constraints require what explainers. We find that no single explainer is able to outperform all the other explainers in all metrics. This work offers practical recommendations for selecting appropriate explainers for specific biomedical applications. It identifies key directions for developing domain-specific explainability approaches that address the unique needs of biomedical research.

Introduction
Paediatric palliative care (PPC) aims to optimise the quality of life of children with life-limiting or life-threatening conditions by addressing physical, psychosocial, emotional and spiritual needs of children as well as their family members. Advance care planning (ACP) is a central element of PPC, as it helps children and family members formulate values, needs, and goals for future care. However, ACP documentation is time-consuming and burdensome for healthcare professionals (HCPs). Large Language Models (LLMs) may support this process by automatically extracting and structuring ACP outcomes. This study explored the support of open-source LLMs summarising ACP outcomes from Individual Care Plans (ICPs) in a Dutch PPC setting.

Methods
We constructed a pseudonymised dataset of 38 ICPs, with reference ACP summaries structured around three guiding questions: (1) Who are you?, (2) What is important to you?, and (3) What are your goals and wishes for future care and treatment? Two open-source decoder-only LLMs were selected: Llama-3.1-8B-instruct (Llama-3.1) and Fietje-2-instruct (Fietje-2). We evaluated their performance under zero-shot prompting, in-context learning (ICL) with up to eight examples, and QLoRA fine-tuning on 30 training samples. Outputs were assessed with automatic metrics (BLEU, ROUGE-L, BERTScore, MEDCON), complemented by textual analysis and a human reader study.

Results

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various applications due to their ability to capture complex structural relationships within graph data. However, their inherent black-box nature poses significant challenges to model interpretability, particularly in the context of link prediction tasks. This thesis investigates the explainability of GNNs for link prediction, a critical task in domains such as social network analysis and recommendation systems. We propose an efficient extension of the Zorro explainer, originally designed for node classification, to address the unique challenges of explaining link predictions. Our method leverages top similar nodes for initialization and incorporates techniques to accelerate the greedy search process, ultimately producing binary node and feature masks as explanations. Additionally, we introduce a novel Edge-level RDT-Fidelity metric to better evaluate explainers that generate edge masks, complementing existing metrics like RDT-Fidelity and Sparsity. Experimental results on the Cora and PubMed datasets demonstrate the superior performance and efficiency of our Zorro extension. This work contributes to the growing field of GNN interpretability by providing new methodologies and evaluation frameworks specifically tailored for link prediction.

Predicting properties, such as toxicity or water solubility of unknown molecules with Graph Neural Networks has applications in drug research. Because of the ethical concerns associated with using artificial intelligence techniques in the medical field, explainable artificial intelligence techniques are used to explain how GNNs make their decisions. To evaluate the performance of those techniques, different metrics are used. The BAGEL benchmark proposes four such metrics, designed to be useable with any GNN explainer. Of these, the applicability of faithfulness was investigated in molecular property prediction, measured by the submetrics of comprehensiveness and sufficiency. While comprehensiveness and sufficiency were designed to be task agnostic, several shortcomings were identified that make it unsuitable for molecular property prediction. Future recommendations are to investigate other pre-established faithfulness metrics or to develop ones that do not require splitting molecules.

Graph neural networks (GNNs), while effective at various tasks on complex graph-structured data, lack interpretability. Post-hoc explainability techniques developed for these GNNs in order to overcome their inherent uninterpretability have been applied to the additional task of detecting important subnetworks in graphs. For example, the GNN-SubNet program uses explanations of protein-protein interaction networks to detect the most important disease subnetworks for specific types of cancer. However, when using a post-hoc explanation for such additional tasks, evaluating the quality of the explanation becomes critical.
This study implements four explainability evaluation metrics to provide a fast and accurate way of evaluating explainability, using the GNN-SubNet program as a case study of explainable GNNs for subnetwork detection. Fidelity and sparsity metrics are implemented as defined in existing literature, while validity+ and validity- are newly defined. The results show that GNN-SubNet finds robust and faithful but highly dense explanations.

As graph neural networks (GNNs) become more frequently used in the biomedical field, there is a growing need to provide insight into how their predictions are made. An algorithm that does this is GNN-SubNet, developed with the aim of detecting disease subnetworks in protein-protein interaction (PPI) networks. GNN-SubNet makes use of a sampling scheme to generate a global explanation in the form of a node mask which indicates each node's importance for all of the GNN's predictions on a dataset. The aim of this study is to validate GNN-SubNet by comparing it with an alternative approach of obtaining global explanations. Instead of obtaining the node mask via a sampling scheme, multiple (local) explanations are optimized per dataset sample, then the node masks are aggregated by either the mean (Mean Aggregation) or the median value (Median Aggregation) per node.
GNN-SubNet is compared with its two modifications firstly by analyzing which disease subnetworks each algorithm detects, and secondly by leveraging metrics devised to assess explainers for GNNs. The results show that all algorithms detect subnetworks associated with cancer. In terms of the metric scores, Mean Aggregation obtains explanations with the highest fidelity, however no algorithm obtains sparse explanations. The study also indicates that GNN-SubNet obtains variate outcomes over multiple runs, and as such the results may not be reproducible.

AI explainers are tools capable of approximating how a neural network arrived at a given predic- tion by providing parts of the input data most rel- evant for the model’s choice. These tools have become a major point of research due to a need for human-verifiable predictions in multiple differ- ent fields, such as biomedical engineering. Graph Neural Networks (GNNs) are often used for such tasks, which led to the development of GNNSub- net, a tool capable of finding disease subnetworks on models trained with protein-protein interaction (PPI) data. This tool has been tested with only a single GNN architecture, which left a knowledge gap about the performance of the tool under dif- ferent models, which can differ significantly in the way they operate. Here the question ”How does the explainer perfor- mance vary with change in architectures of training models?” is answered. This paper explores this knowledge gap by train- ing and evaluating two other models (GCN and GraphSAGE) to see if the explanation performance of GNNSubnet changes. The performance is eval- uated with BAGEL metrics, a tool developed for XAI analysis. These metrics allow for comparison of explanations on multiple benchmarks. Three of these - Fidelity, Validity- and Validity+ - measure how accurate an explanation was in terms of iden- tifiying important nodes. The last one - Sparsity - assesses the nontriviality of an explanation by mea- suring how few nodes have been identified as im- portant. The experimental process shows low performance changes with different GNN architectures for accuracy-related metrics - RDT-Fidelity, Validity- and Validity+, which means that GNNSubnet is highly generalizable and not tied to a specific GNN model. However, the Sparsity score differs across models, with GIN being able to provide the most concise - and therefore useful - explanations.

This study evaluates how the explainer for a Graph Neural Network creates explanations for chemical property prediction tasks. Explanations are masks over input molecules that indicate the importance of atoms and bonds toward the model output. Although these explainers have been evaluated for accuracy, no information exists on how faithful they are to the model (faithfulness), or how closely they correspond to human rationale (plausibility). Using explainability metrics to measure this, the per formance of the explainer is evaluated on different subsets based on the presence of benzene rings and halogens respectively, and on molecular weight. This study reveals that benzene rings influence the plausibility performance of the explainer, showing that performance is better at higher thresholds but worse at lower thresholds. Molecular weight and the presence of halogens do have no impact on plausibility. The ratio of positive samples in a set is shown to influence the metrics used for faithfulness. To ac
curately evaluate the faithfulness of different subsets, they should be changed to have equal positive rates or different metrics should be used. This research can be used as a starting point to research the influence of dataset properties on explainer performance. This is useful to create better explainers, leading to better acceptance of these models.

As music video streaming occupies a significant market share in how people consume music, gaining an understanding of user behavioural patterns becomes increasingly crucial. This understanding can enable better music video streaming experiences by tailoring them towards more personalized and user-centric designs. Though prior works have emphasized user behaviour during solely listening to music, understanding user actions/clicks while consuming music videos remains largely unexplored. Given the unique experience offered by the combination of audio and visual elements, there is a need for focused research in this area.

Therefore this study attempts to bridge this research gap by collecting and analysing a large dataset of streaming sessions from a music video streaming company - XITE. In total, we analyzed 1.8 million sessions from approximately 270,000 unique users. The behaviour exhibited during those sessions is interpreted as a language and modelled using the Language Model - Doc2Vec. This facilitated the conversion of session action sequences into embeddings. Our findings suggest that music video streaming sessions exhibit cohesive user interaction patterns, which can be grouped into distinct clusters, thereby enabling the detection of distinct behavioural patterns across user sessions.


Furthermore, previous studies have indicated that user interactions with multimedia streaming platforms can be influenced by the context in which content is consumed. Extending these findings, our analysis of behavioural clusters revealed that certain user behaviours while consuming music videos are associated with specific music video genres and temporal factors. For instance, we discovered that passive sessions predominantly commence around 10 am, while sessions requiring more active engagement typically start in the evening. The insights derived from this study are valuable for improving user-centric design in music video streaming platforms and providing businesses with data-driven recommendations for strategic planning.

Cell-free DNA (cfDNA) are DNA fragments originating from dying cells that enter the plasma. Uncontrolled cell death, for example caused by cancer, induces an elevated concentration of cfDNA. As a result, determining the cell type origins of cfDNA can provide information about an individual's health. This research looks into how to increase the sensitivity of a methylation-based cell type deconvolution method. We do this by adapting an existing method, CelFiE, which uses the methylation values of individual CpG sites to estimate cell type proportions. Our new method, named CelFEER, instead differentiates cell types by the average methylation values of individual reads. We additionally improved the originally reported performance of CelFiE by using a new approach for finding marker regions in the genome that are differentially methylated between cell types. This approach compares the methylation values over 500 bp regions instead of at single CpG sites and solely takes hypomethylated regions into account. We show that CelFEER estimates cell type proportions with a higher correlation than CelFiE on simulated mixtures of cfDNA. Moreover, we found that it can find a significant difference between skeletal muscle cfDNA in ALS patients (n=4) and a control group (n=4).