Multi-Modal Data Integration for Inflammatory Bowel Disease

Inflammatory Bowel Disease (IBD) is a relapsing and remitting immune-mediated disease characterized by chronic inflammation and damage of the gastrointestinal tract. In the USA alone, it is estimated that at least 58,000 children and 1.2 million adults suffer from IBD, with both incidence and prevalence rising. The main disease entities are Crohn’s Disease (CD) and ulcerative colitis (UC) which present with different clinical and pathological phenotypes but share genetic risk factors and treatment options. Currently, treatment strategies are based on assessment of disease activity and phenotyping of the disease severity including history of complications and disease location. To improve on the clinical classification of IBD and enable precision medicine approaches, we need to go beyond clinical phenotyping, and integrate molecular data with clinical data to better define molecular and cellular subphenotypes that are associated with disease complications and treatment response.

The history of IBD research at the Mount Sinai Health System in New York goes back to 1932 with the first description of Crohn’s Disease by Dr. Burrill B. Crohn and is up until today a major research focus at Mount Sinai (MS). In collaboration with clinician researchers at MS, we aim to develop clinically meaningful machine learning models to potentially forecast the progression of disease and enable clinical sub-phenotyping of the disease. Data of interest includes electronic health records, images and reports from Radiology and Pathology as well as EHR-linked Omics data.

Please reach out in case you are interested in a research internship or master thesis in context of the described project.

• Inflammatory Bowel Disease
• Multi-modal data integration
• Predictive modeling

Li, X., Michels, B.E., Tosun, O.E., Jung, J., Kappes, J., Ibing, S., Nataraj, N.B., Sahay, S., Schneider, M., Wörner, A. and Becki, C., 2022. 5’isomiR-183-5p|+ 2 elicits tumor suppressor activity in a negative feedback loop with E2F1. Journal of Experimental & Clinical Cancer Research, 41(1), https://doi.org/10.1186/s13046-022-02380-8

Ibing, S., Michels, B.E., Mosdzien, M., Meyer, H.R., Feuerbach, L. and Körner, C., 2021. On the impact of batch effect correction in TCGA isomiR expression data. NAR Cancer, 3(1), https://doi.org/10.1093/narcan/zcab007

Giacomelli, C., Jung, J., Wachter, A., Ibing, S., Will, R., Mannsperger, H., Uhlmann, S. et al., 2021. Coordinated regulation of WNT/β-catenin, c-Met, and integrin signalling pathways by miR-193b controls triple negative breast cancer metastatic traits. BMC Cancer, 21(1296), https://doi.org/10.1186/s12885-021-08955-6

Bustos, F.J., Jury, N., Martinez, P., Ampuero, E., Campos, M., Abarzúa, S., Jaramillo, K., Ibing, S., Mardones, M.D., Haensgen, H. and Kzhyshkowska, J., 2017. NMDA receptor subunit composition controls dendritogenesis of hippocampal neurons through CAMKII, CREB‐P, and H3K27ac. Journal of cellular physiology, 232(12), https://doi.org/10.1002/jcp.25843

Since 2020: Research Assistant, PhD student at the Digital Health Center, Hasso Plattner Institute

2019 - 2020: Research Assistant at the German Cancer Research Center, Applied Bioinformatics

2015 - 2019: Molecular Biotechnology (M.Sc.), Heidelberg University
Thesis: "Pan-cancer Analysis To Identify Genetic Factors Causing Differential MicroRNA Processing" at the German Cancer Research Center

2012 - 2015: Molecular Biotechnology (B.Sc.), Heidelberg University