Sonja Katz

• PhD candidate at Wageningen University & Research

Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyping and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging inter-individual heterogeneity. However, the impact of con...

Deep learning applications have had a profound impact on many scientific fields, including functional genomics. Deep learning models can learn complex interactions between and within omics data; however, interpreting and explaining these models can be challenging. Interpretability is essential not only to help progress our understanding of the biol...

Genetic ancestry plays a major role in pharmacogenomics, and a deeper understanding of the genetic diversity among individuals holds immerse promise for reshaping personalized medicine. In this pivotal study, we have conducted a large-scale genomic analysis of 1,136 pharmacogenomic variants employing machine learning algorithms on 3,714 individuals...

The growing interest in clinical diagnostics has recently focused on metabolic biomarkers. Here, we present a protocol for sample preparation, extraction of cholesterol-related sterols, and quantification of 10 sterols in human blood serum samples using targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS). We also describe steps of ma...

Objectives: To develop and externally validate machine learning models for predicting microbial aetiology and clinical endpoints, encompassing surgery, patient management, and organ support in Necrotising Soft Tissue Infections (NSTI). Methods: Predictive models for the presence of Group A Streptococcus (GAS) and for five clinical endpoints (risk o...

Introduction In the evolving healthcare landscape, precision medicine's rise necessitates adaptable doctoral training. The European Union has recognized this and promotes the development of international, training-focused programmes called Innovative Training Networks (ITNs). In this article, we introduce TranSYS, an ITN focused on educating the ne...

In the field of precision medicine, the use of multi-omics data for patient stratification holds great promise for delivering tailored treatments based on comprehensive individual biological profiles. The clinical potential of (multi-)omics data, however, faces significant limitations due to the presence of confounding factors in the data, such as...

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, d...

Despite the wealth of knowledge generated through epigenome-wide association studies our understanding of the relationships of CpG sites is still limited, as analysis of DNA methylation data remains difficult due its high dimensionality. To combat this problem, deep learning algorithms, such as autoencoders, are increasingly applied to capture the...

Introduction Necrotizing Soft Tissue Infections (NSTI) are severe infections with high mortality affecting a heterogeneous patient population. There is a need for a clinical decision support system which predicts outcomes and provides treatment recommendations early in the disease course. Methods To identify relevant clinical needs, interviews wit...

Glioblastoma (GBM) is characterized by a particularly invasive phenotype, supported by oncogenic signals from the fibroblast growth factor (FGF)/ FGF receptor (FGFR) network. However, a possible role of FGFR4 remained elusive so far. Several transcriptomic glioma datasets were analyzed. An extended panel of primary surgical specimen-derived and imm...

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third most common cause of cancer-related death, with tumour associated liver endothelial cells being thought to be major drivers in HCC progression. This study aims to compare the gene expression profiles of tumour endothelial cells from the liver with endothelial cells from no...

Rational-design methods have proven to be a valuable toolkit in the field of protein design. Numerical approaches such as free-energy calculations or QM/MM methods are fit to widen the understanding of a protein-sequence space but require large amounts of computational time and power. Here, we apply an efficient method for free-energy calculations...

The existence of covalent heme to protein bonds is the most striking structural feature of mammalian peroxidases including myeloperoxidase (MPO) and lactoperoxidase (LPO). These autocatalytic posttranslational modifications (PTMs) were shown to strongly influence the biophysical and biochemical properties of these oxidoreductases. Recently, we repo...