Team and publications

Carolin Victoria Schneider, MD is a physician and research group leader. She completed her medical education at the RWTH Aachen University and her postdoctoral fellowship at the University of Pennsylvania. Dr Schneider has received numerous awards and prizes, including admission to the Young Academy of the North Rhine-Westphalia Academy of Sciences and Arts, the Borchers Medal for her dissertation at RWTH Aachen University and the C. A. Ewald Prize of the German Society for Gastroenterology, Digestive and Metabolic Diseases. She has presented her research at various conferences and has won several young investigator awards. Her research focus is on the integration of multi-omics data, lipidomic analyses, and artificial intelligence in the study and prevention of gastrointestinal and metabolic diseases.

Google scholar: https://scholar.google.com/citations?user=svKiqaAAAAAJ&hl=en

Research Gate: https://www.researchgate.net/profile/Carolin-Schneider-7

As a postdoctoral researcher, Dr. Benjamin Laevens supervises PhD students and promotes the integration of citizen science in prevention research. As an astrophysicist by training he bridges the gap between natural sciences, statistics and prevention. His efforts focus on engaging the public in scientific research, fostering collaboration between professional scientists and community members. By incorporating citizen science, Dr. Laevens aims to enhance data collection, broaden research perspectives, and strengthen the connection between science and society. His unique profile strengthens interdisciplinary discourse.

Jan Clusmann, MD is a postdoc and medical doctor focusing on hepatocellular carcinoma (HCC) and is working on developing image analysis techniques using artificial intelligence to improve HCC diagnosis and treatment. His research involves utilizing large-scale omic datasets and developing novel AI techniques to identify key features and biomarkers that are associated with HCC development and progression. Jan is also driving forward the development of scoring systems for HCC in the general population that can be used to guide personalized therapy approaches. His work aims to improve overall HCC diagnosis and treatment outcomes, and to contribute to the development of personalized therapy strategies that can help prevent and treat HCC in high-risk populations.

Yazhou Chen is a PhD candidate at Schneider Lab working on a project that aims to employ large-scale clinical and imaging data to unravel essential pathophysiological pathways linking MAFLD, plasma lipid metabolism, and CVD risk. Yazhou is pioneering the use of radiomics and machine learning to predict genetic variants from medical imaging data. This innovative approach paves the way for the creation of predictive models, aiding in identifying patients with a high risk of these diseases and unveiling potential targets for therapeutic interventions.

Niharika Jakhar is a PhD candidate at Schneider Lab, focusing on a project that seeks to leverage extensive clinical and proteomic data to elucidate crucial pathophysiological mechanisms connecting liver disease progression, protein expression profiles, and associated health risks. Niharika is at the forefront of using proteomics and advanced computational methods to identify protein biomarkers for clinical outcomes. This cutting-edge approach is instrumental in developing predictive models, which are key in identifying patients at high risk of advanced liver diseases and uncovering new possibilities for therapeutic, personalized strategies.

Tobias Seibel is a researcher working on the development of advanced deep learning models for the prediction of hepatic outcomes. His work is centered on creating sophisticated algorithms that can accurately forecast the progression and prognosis of liver diseases. Additionally, Tobias is dedicated to constructing a comprehensive pipeline for the integration of genomic data into these models. This innovative approach aims to enhance the precision of predictions by incorporating a wide range of genetic information, thereby offering a more tailored and effective strategy for the management and treatment of liver-related health issues.

Mara Vell is a postdoctoral researcher whose research focuses on repurposing common medications to prevent liver diseases in the general population. Her work involves analyzing large-scale population-based genetic studies and lipidomic data to identify optimal targets for therapeutic interventions. Additionally, Mara is also exploring personalized treatment options for different populations, with a particular focus on sex differences in regard to medication response. Her research aims to contribute to the development of personalized prevention and treatment strategies for liver diseases, and to improve overall liver health outcomes in different populations.

Miriam Rendel is an MD candidate investigating ERLIN1 as a potential treatment target for metabolically-associated fatty liver disease. Her methodology includes exploring the effect of different loss of function mutations in ERLIN1 and their relationship with fatty liver disease, as well as utilizing in vitro and in vivo experiments to validate the potential efficacy of targeting ERLIN1 in treating MAFLD.

Leonida Hehl is a postdoc investigating the female liver health and sex differences for preventing fatty liver disease. Her methodology involves leveraging large-scale genetic datasets to identify genetic variants associated with a sex-specific decreased risk of fatty liver disease. She is also conducting functional studies to investigate the potential mechanisms underlying the protective effects of these genetic variants.

Katharina Seeling is an MD candidate studying mortality and morbidity in patients with irritable bowel syndrome (IBS). Her methodology includes analyzing large-scale population-based datasets to identify potential risk factors for increased mortality and morbidity in IBS patients, as well as conducting studies to investigate potential biomarkers for IBS in the general population.

Simon Schophaus is a postdoctoral researcher exploring personalized nutrition as a potential intervention for preventing liver disease. His methodology includes analyzing large-scale dietary and health datasets to identify potential personalized nutrition interventions that may be effective in reducing the risk of liver disease, as well as conducting clinical studies to validate the efficacy of these interventions.

Carola Dlugosch is a dedicated MD student specializing in the complex interplay between steatosis and cancerogenesis, with a focus on colorectal cancer. Her work primarily revolves around unraveling the intricate connections within the Gut-Liver axis, aiming to shed light on how liver conditions like steatosis can influence the development and progression of cancers, like colorectal cancer.

A recipient of the prestigious MD scholarship from the SFB (Sonderforschungsbereich) Gut Liver Axis, Carola brings an innovative and inquisitive approach to her research. Through her research, Carola aims to contribute significantly to the development of new diagnostic and therapeutic strategies, potentially leading to better outcomes for patients suffering from these conditions.

Felix van Haag is an MD candidate engaged in pioneering work to predict cholangiocarcinoma using multimodal data, including metabolomics, proteomics, and other relevant biological markers. His research involves integrating diverse datasets to create a comprehensive picture of the heterogeneous group of cholangiocarcinomas, aiming to identify key biomarkers and patterns associated with the onset and progression of cholangiocarcinoma. By employing a holistic approach that combines various types of biological data, Felix's work will significantly enhance the accuracy of early detection methods and improve the understanding of the disease's pathophysiology, ultimately contributing to more effective treatment strategies and patient outcomes.

Paul Koop, postdoc at Schneider Lab, is blazing trails with his work on Python to develop algorithms for managing and visualizing PheWAS data from the UK Biobank. Specializing in the nexus of computer science and epidemiology, Paul is leveraging Python's powerful data analysis capabilities to unlock new understandings. His project focuses on developing unique algorithms to sift through the extensive PheWAS data. This work is instrumental in streamlining data analysis and pinpointing critical insights within the vast dataset. Furthermore, Paul is translating these complex datasets into intuitive visual formats, facilitating easier comprehension and utilization of the data. Paul's expertise is reshaping how Schneider Lab approaches PheWAS data.

As a PhD student, Thriveni Basavanapura Raju is engaged in a groundbreaking research project that focuses on integrating large-scale microbiome data using the Lifelines cohort. Her work is centered around exploring the intricate interplay between nutrition, the microbiome, and liver disease. By analyzing extensive data sets, Thriveni aims to uncover how dietary patterns influence the gut microbiome and, in turn, how these microbial communities impact liver health. This research is crucial for understanding the complex interactions between diet, gut flora, and liver disease, potentially leading to innovative approaches in the prevention and management of liver-related disorders.

As a physician, Joel investigates the epidemiology of liver diseases and thyroid disorders, with a particular focus on the impact of medications in these areas. His research aims to understand the prevalence, risk factors, and long-term health outcomes associated with these conditions, exploring how different treatment regimens influence disease progression and patient health.

As an MD student, Sarah is researching whether high-cost omic techniques are essential for accurately predicting liver disease. Her work critically evaluates the effectiveness of these advanced tools compared to more accessible diagnostic methods, aiming to determine if they truly improve patient outcomes or if more cost-effective options could suffice. By examining diagnostic accuracy and cost-benefit analysis, Sarah’s research could guide clinical practices toward sustainable and evidence-based use of resources in liver disease prediction.

As a master's student in economics, Lorenzo focuses on the economic aspects of prevention and artificial intelligence in healthcare. His research examines the cost-effectiveness and long-term economic benefits of preventive strategies, especially when integrated with AI-driven tools. By analyzing data on healthcare costs and outcomes, Lorenzo aims to highlight how AI can enhance preventive care, ultimately supporting more efficient healthcare systems and better patient outcomes. 

As a master’s student, Isabelle  is developing an advanced genetic integration pipeline, which uses data from various genomic sources to increase our understanding of gene-disease associations. Her project focuses on creating a streamlined method for merging large-scale genetic datasets, enabling more efficient identification of genetic factors that contribute to complex diseases. Through this pipeline, Isabelle aims to improve the accuracy and reliability of genetic analyses, supporting researchers in uncovering new insights into disease mechanisms. 

As a master's student, Alejandra  is conducting research on lipoproteins to gain insights into liver biology. Her work focuses on understanding the role of lipoproteins in hepatic lipid metabolism and their impact on liver function. By analyzing the synthesis, secretion, and regulation of various lipoprotein classes, Alejandra aims to elucidate their contributions to lipid transport and storage within the liver. 

As a master's student, Abdallah is developing a machine learning strategy to identify promising drugs for repurposing in the treatment of liver diseases. His research involves analyzing extensive datasets to uncover potential therapeutic agents among existing medications. By employing advanced computational techniques, Abdallah aims to streamline the drug discovery process, potentially accelerating the availability of effective treatments for liver-related conditions.

As a MD student, Anastasia researches high-risk MASLD patients to identify which individuals are most vulnerable to disease complications and progression. Her work examines patient characteristics and risk factors to better understand the pathways that lead to advanced stages of the disease. Through this research, Anastasia aims to provide insights that could help clinicians identify at-risk patients earlier and improve targeted prevention and treatment strategies for MASLD.

Helen Huang, a dedicated rotation student at the University of Pennsylvania and closely associated with Schneider Lab, is making significant contributions to the field of genetic research on Metabolic-Associated Fatty Liver Disease (MAFLD). She is leveraging the wealth of data available from the Penn Medicine Biobank to unearth genetic targets for MAFLD, aiding our understanding and potential treatment of this pervasive condition. Her work bridges the gap between big data and medical research, contributing valuable insights to our lab's comprehensive exploration of metabolic and gastrointestinal diseases.