The human gut microbiome has emerged as a key player in health and disease, yet machine learning on microbiome data remains challenging due to its high dimensionality, sparsity, compositionality, and inter-study heterogeneity. Although classical and deep learning methods have demonstrated promise, they often require extensive labeled data, which is rarely available in microbiome research. In this thesis, we investigate whether meta-learning can address these challenges by enabling better generalization from small, heterogeneous microbiome datasets. Specifically, we benchmark Prototypical networks (Protonets), a metric-based, few-shot meta-learning algorithm, against strong classical baselines (Random Forests, XGBoost, and Multi-layer Perceptrons) for disease classification tasks across a selected number of gut microbiome studies. We introduce a unified benchmarking pipeline that standardizes preprocessing, dimensionality reduction, task construction, and evaluation across studies. A leave-one-study-out cross-validation strategy simulates realistic deployment scenarios where only a few labeled samples are available from a new cohort. Our experiments explore the impact of support set size and dimensionality reduction via principal component analysis. Results show that although Protonets offer a conceptually appealing approach for few-shot learning, they consistently underperform compared to Random Forests in classification accuracy. Statistical analyses confirm the significance of this performance gap, and embedding visualizations reveal limited class separation in the learned feature space. These findings suggest that, under the evaluated conditions, classical models like Random Forests remain the more robust choice for microbiome classification in low-data regimes. By offering a rigorous and reproducible evaluation, this work lays the foundation for further exploration of meta-learning in microbiome research and highlights both the potential and current limitations of learning to learn in this complex domain.

In the past decade, protein functional prediction has dramatically shifted towards the usage of large language models (LLMs). In this research, we set out to improve upon the model of SAFPred, a model for prokaryote protein function prediction combining LLM embedding based sequence homology prediction with a synteny aware component. With a new technique referred to as stopping layers, we successfully proved that we can remove layers from pre-trained LLMs without sacrificing performance, ultimately giving us the ability to reduce runtime by 70% and required GPU VRAM usage for LLM storage by 72%. Furthermore, we show that we can prune our training dataset by only using prokaryote proteins without any performance impact, reducing the training set by 78%. Additionally, in our evaluated models we show that restricting the amount of training matches per query protein as much as possible is beneficial to model performance.

Antimicrobial resistance (AMR), termed a "silent pandemic" has caused 4.95 million deaths in 2019, with numbers expected to rise. AMR spans human, animal, and environmental sectors, requiring a One Health approach to address this multifaceted global challenge. This dissertation focuses on the under-represented non-clinical sectors and employs the use of metagenomic data to advance AMR research.

The primary focus in AMR research has been on clinical settings, overlooking animals and the environment and leaving data gaps in resource-limited regions. The world of AMR and metagenomic data is first introduced followed by an in-depth review of AMR in non-clinical sectors and the information metagenomic data can provide. The emphasis is on bioinformatic tools, databases, and workflows to support researchers utilising metagenomic data for AMR studies in these sectors.

Moving forward, the wastewater treatment process, including the neglected upstream and downstream freshwater systems, is examined, to assess the microbiome, resistome and mobilome at each stage. Specific differences within every wastewater treatment plant process sector and their role in AMR transmission are identified. Inspired by the natural baseline of antibiotic resistance in soil, a comparative study of the composition of the microbiome, resistome and mobilome in different soil types, from natural to rural soils, is then further presented. Given the limited information on resistance patterns and the effects of geographical and anthropogenic factors, the influence of antibiotic resistance in different soil types is then further explored.

The swine industry, as the largest consumer of antibiotics, raises concerns about the effects of antibiotic use on the gut microbiome of animals. Antibiotics can impact animal health and promote the transmission of AMR to other non-clinical sectors and humans. How antibiotic use affects the fecal microbiome of pigs raised with and without antibiotics is examined to understand the dynamics of antibiotic resistance in the swine industry.

The burden of AMR, particularly in low- and middle-income countries, where resources for infectious disease surveillance are limited, was the inspiration to propose a method for generating metagenomic data in-field and in resource-limited settings, offering a cost-effective solution for outbreak monitoring and pathogen detection.

The main goal of this dissertation is to highlight the under-represented sectors, their significant role in AMR and to promote global inclusivity.

Artificial intelligence (AI) has become a widely discussed and transformative technology, with its adoption growing across industries to drive insights and impact. In this thesis, we explore how AI methods and algorithms can facilitate the operation of soft-fruit supply chains, using strawberries as a case study.

The thesis begins by presenting the general background and various perspectives from related works on how AI and machine learning (ML) have been applied to address problems in agricultural or horticultural practices. This includes tasks that, while not directly optimizing supply strategies, still contribute to solving broader challenges. In a nutshell, this thesis categorizes the scope of study into three scales: the single-fruit scale, the greenhouse scale, and the market scale. Within each scale, we review the existing research, identify knowledge gaps, and introduce robust and applicable methodologies capable of dealing with real-world conditions.

Since no publicly available datasets met the requirements of the research plan, we established several datasets for research on the soft-fruit supply chain through collecting, annotating, and (pre-)processing data. These newly curated datasets not only support the research presented in this thesis but also lay a foundation for future research from various perspectives. Details about these datasets are introduced in Chapter 2. Moreover, we conceptualize the process of gathering longitudinal observations from growth monitoring images as a multiple object tracking (MOT) task. We named the image collection and their MOT annotations as ``The Growing Strawberries (GSD)''. The computer vision challenge that GSD brings are further benchmarked and discussed in Chapter 3. Following this, the core contributions of the thesis is presented from Chapter 3 to 6, each corresponding to a published paper or one currently under review. Finally, Chapter 7 summarizes the research findings, answering the research questions proposed in Chapter 1 and discussing the overall work of the thesis.

We discuss these contributions for each of the three mentioned scales separately:

At the fruit scale, we designed and analyzed novel methodologies to keep track of the fruit growths and to predict key properties, including both external characteristics like ripeness and internal qualities such as sweetness. For the ripeness, we propose to use appearance properties, mainly the hue, as an objective metric to quantify it. For the sweetness, we trained deep neural networks to perform non-destructive prediction using environmental and image data, individually and integrally.

Our employment of color analysis and ML models provides a non-destructive and generalizable manner that ensures consistency when upstream and downstream parties in a supply chain estimate the properties of fruits. Meanwhile, the models perform comparatively with laboratory benchmarks even under imperfect, outdoor data collection. We further demonstrated the model in a mobile app to further facilitate adoption in the field.

By benchmarking state-of-the-art MOT algorithms on GSD, we illustrated the new challenges that are brought by this use case: first, the MOT objects change appearance during the tracking due to their biological development, and second, sparse frame rates introduce irregular movements from image to image. We showcased how fruit properties, such as ripeness, change over its life cycle. The results not only provide quantitative measurements that describe the fruit's biological development, but also depict the pain points of current MOT algorithms' predictions. In the meantime, by quantifying these changes over the biological development, we also retrieval relevant information and datasets to support predictions of the changes.

At the greenhouse scale, we designed a framework that optimizes the timing of fruit harvesting by integrating the aforementioned quantified changes over biological development, based on sequential demands about the desired quantities to be harvested. Essentially, the framework makes fruit-specific decisions on dates of harvests by leveraging the monitoring data. The decisions are thus made to enhance both current and future demand-fulfillment capabilities. At each stage of this framework, we evaluated various methods and discussed their effectiveness in achieving the stage targets. For example, how to process the infield data to achieve coherent functions about the ripeness development, how to predict future changes, how to include different perspectives in the optimization model, and etc. As the decisions are made for each specific fruit, the work also demonstrates significant potential for integration with mobile apps and harvesting robots. On top of that, the information retrieval function can also serve as a standalone application to provide objective fruit-level quality assessment.

At the market scale, we focus on the portfolio optimization of a grower under a widely applied mechanism of the market system: the majority of demands for harvests are predetermined through advance contracts, which also serves as an a priori condition of the solution proposed at the greenhouse level. The local market, with dynamic prices and demands, can be used to save losses from the difference in contracted demands and the actual yield. To mitigate outlying decision failures, we introduced the ``smart predict-then-optimize (SPO)'' method, which trains models to predict future yield and local market prices.
Our results illustrate that SPO loss primarily affects the bias layer in neural networks, contrasting with models trained using mean squared error (MSE). This difference essentially leads to more conservative estimations in decision-making scenarios, and also motivates and highlights the importance of effective MSE-based pre-training. Additionally, our study reveals how SPO loss makes models interact when multiple neural networks are trained to predict decision parameters with diverse functions. This insight expands the applicability of SPO loss across a broader range of use cases and model architectures, underscoring its contribution to the field of decision-focused learning.

In conclusion, this thesis introduces diverse data-driven methodologies to tackle the distinct tasks involved in optimizing fruit supply, using strawberries as a case study. Central to our approach is the effective utilization of data, which serves as the foundation for solutions that span from fruit-level evaluations to market-level planning. By leveraging analytics of non-destructive data, our solutions provide objective estimations of fruit quality, fostering a more consistent shared understanding between sellers and buyers while reducing potential food waste. Overall, these advancements push the boundaries of AI in supporting decision-making during the supply of soft fruits, particularly for smaller growers. The findings not only empower more efficient and sustainable supply chain operations but also highlight the strong potential for many practical real-world applications.

Bacteria are everywhere and play essential roles in Earth's diverse ecosystems and human health. For example, humans harbor a complex and essential gut microbial community comprising thousands of bacterial species (in addition to numerous viruses, fungi, and microbial eukaryotes). This community helps break down and synthesize nutrients, trains the immune system, and keeps pathogens at bay. However, imbalances in this community are associated with several diseases, including obesity, inflammatory bowel syndrome, and recurrent urinary tract infections. Moreover, bacteria can cause deadly infections, and many are developing resistance to our most potent antibiotics. Studying bacteria is thus essential for identifying differences between pathogens and harmless commensals, countering antimicrobial resistance, and understanding their impacts on human health.

To study bacteria, we typically characterize and compare their genomes. The genome comprises all of an organism's hereditary information, and its genes encode the molecular machines necessary for cell function, providing an overview of an organism's capabilities. The hereditary information in genomes additionally enables inferring the organism's evolutionary history, which helps in understanding why specific traits evolved or aid in inferring transmission links in case of an outbreak.

A challenge with comparing large sets of bacterial genomes is the extensive variation in genome content among many species. For example, two Escherichia coli strains can share as little as 50% of their genes. Current computational tools offer biased or incomplete views of genetic variation among strains. This hinders the identification of genotype-phenotype associations, prevents tracking mobile genetic elements, and limits our understanding of the microbial communities they are part of.

The central question of this thesis is how to design computational tools that enable accurate characterization of genetic variation among diverse bacterial genomes. This thesis introduces new algorithms to identify and represent genetic variation using graph data structures. It additionally presents a tool that characterizes strain-specific genetic variation in microbial communities, even in the presence of same-species strain mixtures. Finally, this thesis uses the previously mentioned tools to investigate the role of the gut microbiome in women with recurrent urinary tract infections, offering novel insights into the gut and bladder dynamics of  E. coli.

Collectively, we expect this work to contribute to an improved mechanistic understanding of bacteria's role in human health, help track and counter the spread of antimicrobial resistance, and inform on the development of microbiome-mediated therapeutics.

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.

Motivation: Imbalances in the human gut microbiome have been linked to various conditions, including inflammatory bowel disease (IBD), diabetes, and mental health disorders. While metagenomics and amplicon sequencing are the most commonly used technologies to characterize microbial communities, they do not capture all layers of functional activity of the microbiome. Unfortunately, data from other meta-omics modalities is generally difficult to obtain, due to high costs and error-prone technologies, among other issues. The growing availability of paired meta-omics data offers an opportunity to develop machine learning models that can infer connections between metagenomics data and other forms of meta-omics data. The aim is to enable the prediction of these other forms of meta-omics data from metagenomics data. To that end, we evaluated several machine learning model architectures on the task of predicting meta-omics features from various meta-omics inputs, and analyzed the robustness of these models, as well as potential use cases of artificially generated microbiome data.

Results: Machine learning models, in particular simpler architectures such as elastic net regression models and random forests, generated reliable predictions of transcript and metabolite abundances, with correlations of up to 0.77 and 0.74, respectively, but predicting protein profiles proved more difficult, with correlations of at most 0.42. We also identified a core set of well-predicted features for each meta-omics output type, and showed that multi-output regression neural networks performed similarly when trained using fewer output features. Lastly, our experiments demonstrated that predicted features can be used for the downstream task of IBD prediction. For instance, accuracy obtained using predicted metabolite abundances was 77%, compared to the 80% accuracy achieved using real metabolomics data.

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.

Background: Genetic information is shared between different bacteria through mobile genetic elements, among which plasmids. Some plasmids are able to transfer and spread genetic information between different species. Understanding which genes allow plasmids to replicate in different species is useful in containing the antibiotic resistance epidemic, as well as aid in the creation of vectors in the field of genetic engineering. Which characteristics determine plasmid host range is not fully known. Current approaches either use nucleotide composition or a combination of the genes, also called a genetic backbone, comprising replication and conjugation machinery to predict plasmid host range, but these methods are not perfect in their predictions. As research has shown that plasmids have a certain genetic backbone, we test the hypothesis that conserved regions around replication, conjugation genes and post-segregational killing systems affect host range.

We found that the genes in our conserved regions were almost exclusively encountered in that specific order. When studying the functional annotations of genes in a conserved region, they seem to be functionally related and serve a more grand biological function. Although the replication and conjugation machinery, which are often used as predictors for host range, were found to be associated with plasmid host range, the conserved regions were generally similarly or more specific than the combination of these predictors, which suggests that some conserved regions affect plasmid host range. Several genes related to evading bacterial immune systems and partitioning systems were found to be less conserved, with more divergence in their sequence, for which we hypothesize that these genes usually mutate to ameliorate the cost of plasmid carriage.

This research explores the landscape of dataset generation through the lens of Probabilistic Principal Component Analysis (PPCA) and β-Conditional Variational Auto-encoder (β-CVAE) models. We conduct a comparative analysis of their respective capabilities in reproducing datasets that mirror the distribution of the original data that comes from a hypothetical pathway kinetic model based on an E.coli strain using varied parameter settings falling within a specified range. The requirement of significant prior investment in acquiring accurate details about the distinct mechanisms governing each reaction and its parameters for the construction of these kinetic models push us to find alternative ways to generate data that guide metabolic engineering processes. This paper tries to find a viable option through compression algorithms that reduce dimensionality. The PPCA model demonstrates commendable fidelity in capturing overarching patterns, though areas for refinement in reproducing specific data points are identified. In contrast, the β-CVAE model exhibits higher fidelity, precision, and consistency, positioning it as a robust choice for data generation tasks. This study was constrained by both time and the specificity of the model architectures and the dataset. These limitations underscore the imperative for continual exploration and refinement within the dynamic landscape of generative modeling. Opportunities could be found in the refinement of both VAE, CVAE and β-CVAE models utilizing varied hyperparameters alongside different architectures, to increase applicability across diverse datasets within the realm of metabolic engineering.

This study investigates the application of generative models for synthetic data generation in pathway optimization experiments within the field of metabolic engineering. Conditional Variational Autoencoders (CVAEs) use neural networks and latent variable distributions to generate new, plausible data samples. We adapt this model by conditioning the training process on the target flux to acquire increased performance.

Additionally, a baseline model, namely Probabilistic Principal Component Analysis (PPCA), was selected for a comparative analysis to generate the underlying latent space to test the hypothesis that a type of Variational Autoencoder (VAE) can be used to learn a reduced-dimensional latent space for configurations of a kinetic pathway model. A dataset comprising 5000 hypothetical configurations of a kinetic pathway model was utilized to extract relationships between elements of a kinetic pathway.

The results indicate that PPCA can model the underlying distribution of the dataset when the latent space is large enough. However, the traditional CVAE might struggle to capture the underlying distribution, resulting in an entangled latent space. The study suggests that an implementation of $\beta$-CVAE could lead to a better balance between parts of the objective function during training, offering improved prospects for generating cost-efficient kinetic pathways for combinatorial pathway optimization experiments.

This research investigates the application of Generative Adversarial Networks (GANs) and probabilistic Principal Component Analysis (PPCA) in generating synthetic data for pathway optimization in metabolic engineering. The study aims to compare the performance of these generative models, addressing key questions regarding their utilization, the quality of generated data compared to experimental data, and overall efficiency. The dataset comprises 5000 parameter configurations of kinetic models that simulate a hypothetical pathway. Constructing kinetic models traditionally involves obtaining complex scientific knowledge, a process that may be alleviated through a data-driven approach. Results indicate that both models, tried with different sizes of latent space, demonstrate good performance in modeling the underlying latent space of the data. However, GANs with the right set of parameters exhibit a better performance, evidenced by lower KL divergence and superior visual structure in the generated data. The findings highlight the potential of GANs to outperform probabilistic PCA, offering valuable insights for more cost-effective and streamlined strain optimization in metabolic engineering. Overall, this research advocates for further investigation of GANs capabilities in metabolic engineering as a potentially powerful tool for synthetic data generation.

We are witnessing an era of rapid technological advancements, which led to an explosion in the amount of genomic data collected. The field of comparative genomics, in parallel, is expanding at an unrepentant rate. Comparative genomics explores the similarities and differences in the genomes of various organisms, species or strains, and it is one of our most useful tools today for unraveling the complexities of microbial biology. However, despite growing interest in microbial genomics, there remains a significant gap in our understanding of microbial diversity and function. The microbial dark matter remains elusive, and we have a lot more to uncover.
This dissertation aims to leverage comparative genomics, and develop novel algorithms tailored for microbial genomes to enhance our understanding of microbial biology and address existing knowledge gaps. More specifically, it focuses on the representation of microbial diversity and the functional annotation of poorly characterized taxa. By harnessing large-scale genomic datasets, novel approaches and algorithms are designed to uncover hidden traits in microorganisms.
We begin our journey at the smallest scale with viruses; our study of SARS-CoV-2 genomes in the Netherlands during the COVID-19 pandemic showcases the power of genomic data to understand disease dynamics. The remainder of the dissertation concerns bacteria. We explore pangenome graphs to represent bacterial populations. As I discuss the limitations of current methods, I propose an ensemble approach to exploit graph representations for structural variant calling. This work sets the stage for future developments in pangenome graphs as a powerful framework to model bacterial populations and analyze their genetic makeup.
Following recent developments in algorithms for eukaryotes, I draw inspiration from natural language processing to predict gene functions in bacteria. I present SAFPred, a novel tool in which I integrate bacterial synteny into the predictive model, and demonstrate its use to identify variants of toxin genes in Enterococcus. The novelty of my approach lies partly in how I incorporated bacterial synteny into the function prediction algorithm. Thus, I also release our synteny database, SAFPredDB, that can facilitate various comparative genomic analyses in the future. Our journey comes to an end in our study of the Enterococcus genus through the largest collection of genome assemblies. Here, I emphasize the importance of understanding microbial diversity and antibiotic resistance mechanisms once again, and note the power of large scale genomic analyses.
Overall, my main goal with this dissertation is to showcase the potential of comparative genomics in unraveling the mysteries of microbial life and addressing pressing global challenges in health, agriculture, and biotechnology. Through innovative methods and large-scale data analysis, my work, first and foremost, offers valuable insights into microbial biology and evolution, paving the way for future research in the field. And I hope it also encourages further exploration and appreciation of the mighty world of microbes.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor function loss and potential mental and behavioral changes. The identification of biomarkers in the gut microbiota of PD patients can significantly aid in fast and accurate diagnosis. This study investigates the application of machine learning (ML) models, including Logistic Regression (LR), Random Forest (RF), and Support Vector Machines (SVM), to discover biomarkers in the gut metagenomic data of PD patients. The ML models were optimized using various feature selection techniques, and a comparative analysis of the most influential species in sample discrimination was conducted to verify potential PD-associated biomarkers.
The results demonstrate that all three ML models exhibit moderate performance, indicating their limited discriminatory power. However, the comparison of significant species across different classifiers demonstrates substantial overlap and indicates PD-associated species that align with existing literature findings. These outcomes provide promising evidence that LR, RF, and SVM classifiers can effectively identify biomarkers for PD. However, confounding analysis on a small subset of the dataset failed to identify meaningful PD-associated species. Therefore, caution is advised when interpreting the findings of ML model, considering factors such as classifier performance, dataset limitations, potential biases, influence of feature selection methods, and inherent model differences.
We validate the potential usefulness of ML approaches for biomarker discovery and highlight areas for further investigation into building a sufficiently accurate ML model for PD diagnosis.

Celiac disease is a genetic autoimmune disorder caused by a negative reaction to gluten associated with alterations in the gut microbiome. This study explored the potential of machine learning models and feature selection methods in identifying biomarkers for celiac disease using gut microbiome data. The performance of several machine learning models was evaluated, and the impact of different feature selection methods, including MRMR, ANOVA, and information gain, was examined. The findings revealed comparable performance among the models without feature selection. However, the choice of feature selection method had varying effects on model performance, with logistic regression and support vector machines being more sensitive than random forest and XGBoost models. Notably, several identified bacteria species, such as Bacteroides eggerthii, Parabacteroides johnsonii, Faecalibacterium prausnitzii, and Ruminococcus_D bicirculans, have been previously associated with celiac disease, reinforcing their potential as biomarkers.

Colorectal cancer (CRC), one of the leading causes of mortality, is challenging to diagnose. By using metagenomic analysis with machine learning methods, this can be done in a non-invasive manner. In this research, a neural network has been trained on relative pathway abundance data, a way to measure the functional potential of a microbiome, in order to find biomarkers for colorectal cancer. The accuracy achieved by the neural network is 57%. The most important features used by the model are compared to established biomarkers in literature. Besides overlapping pathways, this research also found new potential biomarkers for CRC.

Type 2 Diabetes is a very prevalent disease in current times and leads to significant adverse effects. Recently, there has been a growing interest in the association of the human gut microbiome with respect to chronic diseases like Type 2 Diabetes with the aim to identify biomarkers. In this study, we researched the effect of different machine learning and feature selection techniques to identify biomarkers for Type 2 Diabetes that can later be used for diagnosis and prediction. The main methods that we explored were Random Forests,Linear Regression, Support Vector Machines andXGBoost along with mRMR and CMIM as feature selection techniques. These methods were applied to data taken from Europe and China. We found that mRMR improved the performance of the Random Forest classifier compared to CMIM.Apart from finding biomarkers specific to one location, we found that Clostridiales, Clostridium, Roseburia and Lactobacillus could be of interestin the prediction of Type 2 Diabetes irrespective of location. This study verified biomarkers found in previous literature and evaluated several techniquesfor the prediction of the disease across different regions.

There is mounting evidence indicating a relation- ship between the gut microbiome composition and the development of mental diseases but the mech- anisms remain unclear. Shotgun sequenced data from 90 schizophrenic patients and 81 sex, age, weight, and location matched controls was used for three machine learning models: Logistic Re- gression, Random Forests, and XGBoost. The 20 most relevant species in the decision mak- ing of each classifier was retained and the over- lap between models recorded. There is a total 19 overlapping species between the models’ top 20 most relevant species, with 10 species over- lapping on all three models. Bifidobacterium bi- fidum, Akkermansia muciniphila, Eubacterium sir- aeum, Alistipes finegoldii, Intestinibacter bartlet- tii, Bifidobacterium pseudocatenulatum, and Strep- tococcus thermophilus are of particular interest as they are reported as enriched in schizophrenia sam- ples in existing literatures. Phoceicola vulgatus has been found to play a significant role in the classi- fiers decisions and is enriched in healthy samples in the literature. One species, Ruthenibacterium lactatiformans, and one co-abundant gene group, Eubacterium sp. CAG:180, consistently ranked as the most important features across all three classi- fiers, despite the absence of reporting in existing literature. This study could be expanded by using genus-level data. Further research should be done to validate the species mentioned above as potential biomarkers for schizophrenia.

Horizontal gene transfer (HGT) trough plasmids is one of the main contributors to the rapid increase of antimicrobial resistance (AMR). Studying wastewater from wastewater treatment plants (WWTPs) allows us new insights into HGT as bacteria from different sources come together. Currently the analysis of HGT is limited, as plasmids cannot be linked to their host species with only metagenomic samples, however when combined with Hi-C sequencing data, sequences from the same cell can be linked together.
We developed a method that uses metagenomic Hi-C data to link bacterial genera together with detected plasmid consensus clusters and resistance genes. Using this method, we analysed datasets from two sources: activated sludge put into a reactor with an antibiotic pressure and a WWTP entrance. The activated sludge dataset was sequenced at two timepoints with an increasing antibiotic concentration. This allowed us to compare degrees of antibiotic resistance in different antibiotic pressures as well as detect broad host-range resistant plasmids. We detected an increase in acquired resistance in environments with a higher antibiotic pressure and detected a resistant plasmid in both locations, linked to both pathogenic as well as bacteria found in active sludge.

Metabolic engineering is an important field in biotechnology, aimed at optimizing cellular processes to produce desired compounds. In this thesis, we focus on predicting the metabolome from the proteome, as understanding this relationship is crucial for understanding cellular metabolism. We investigate the usage of additional biological information like protein-protein interactions and cellular stoichiometry to improve the predictive performance of metabolome prediction models. We also employ explanation algorithms to gain key insights into the regulatory processes of a yeast cell.

We demonstrate the effectiveness of our approach by predicting the metabolic fold-change of multiple yeast kinase knockouts. Our results show that incorporating additional biological information does not significantly improve the accuracy of the metabolome prediction models. Furthermore, we identified enzymes that are relevant for all metabolites used in this study, which indicates the existence of a global set of regulatory enzymes.

Overall, our study shows that through careful manipulation of the limit amount of data decent performance can be expected when predicting the metabolome. We apply a broad spectrum of machine learning algorithms to identify optimal model architecture. The methods and insights presented in this thesis could be used for creating a general pipeline for predicting a broad spectrum of metabolites from the proteome.