Title: Biomarker Prediction from Genomic Sequencing Data
Duration: 01.08.2023 – 31.07.2027
Research Area: Bioinformatics
Our cross-disciplinary project involves different concepts of Systems Biology, Bioinformatics, and Data science, in which we integrate different omics data levels (e.g., DNA, RNA). In particular, we investigate various aspects at the molecular level like the response of cancer cells to ionizing radiation (IR) or the role of the immune system due to cardiac regeneration, among many others. By this means, we analyze heterogeneous data (e.g., DNA-seq and RNA-seq) from databases like TCGA, GEO, and data obtained from experimental partners to understand the impact of different molecular profiles for guiding more personalized treatment strategies.
Different AI approaches will analyze large-scale genomic data to identify key molecular pathways affected by different treatments like IR or stem cell transplantation, discern patterns in genetic or regulatory changes, and predict the effectiveness of IR therapy based on specific tumor profiles. By integrating AI into this research, the project can achieve more precise and comprehensive insights into personalized cancer treatment strategies.
This project will combine current sequencing technologies with Systems Biology approaches and predictive AI approaches, such as AI-guided diffusion network prediction and topology network predictions that might be instrumental in understanding complex cellular responses.
Our work is embedded around the BMBF-funded project OLCIR, in which we will contribute towards the more in-depth investigation of the molecular genetic mechanisms of lung cancer cells and analyze the current AI capabilities to predict established and novel diagnostic markers leading to more personalized therapies.