CAIMed Meet-up AI Powered Approaches in Infection Research
We are pleased to invite you to the upcoming CAIMed Meet-up! This time, the focus is on the use of artificial intelligence (AI) in infection research. The discussion will cover how AI-based methods can help to better understand infection processes and open up new therapeutic avenues.
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- TWINCORE
Agenda
1:00 p.m. – 1:10 p.m. Welcome & Introduction (Prof. Dr. Yang Li, CAIMed, CiiM)
1:10 p.m. – 1:40 p.m. Session 1
AI systems for integrative Multi-Omics Data (Prof. Dr. Tim Beißbarth, University Medical Center Göttingen)
Exploring how artificial intelligence integrates multi-omics data to uncover complex biological interactions and accelerate insights into infection mechanisms.
1:40 p.m. – 2:10 p.m. Session 2
Defense against respiratory pathogens (Dr.-Ing. Geraldine Nouailles, Charité - Universitätsmedizin Berlin)
Highlighting the use of AI to enhance understanding of immune responses and develop innovative strategies to combat respiratory infections.
2:10 p.m. – 2:40 p.m. Session 3
Machine learning applications to bacterial genomics, from essential genes to antimicrobial resistance (Prof. Dr. Marco Galardini, Twincore, MHH)
Demonstrating machine learning's role in identifying essential bacterial genes and uncovering mechanisms behind antimicrobial resistance.
2:40 p.m. – 3:00 p.m. Coffee Break
3:00 p.m. – 4:20 p.m. Session 4
Cutting-edge tocology powered by AI in infection research (CAIMed, Junior Research Group on AI & Bioinformatics)
1. From big data to immunological insights, Dr. Saumya Kumar
Leveraging big data analytics to decode immune system responses and provide a deeper understanding of infection biology.
2. AI and gene regulatory network analysis: a practical introduction, Dr.-Ing. Jalil Nourisa
Introducing AI-driven approaches for analyzing gene regulatory networks, paving the way for insights into genetic regulation during infections.
3. Spatial transcriptomics reveals abnormal immune and cell-matrix remodeling programs in MTB granuloma formation, Dr. Xun Jiang
An example to show what Spatial transcriptomics can bring to infection research.
4. AI powered segmentation and deconvolution in Spatial transcriptomics data analysisa, Yuesi Xi, M.Sc.
Presenting advanced AI tools for segmenting and deconvoluting spatial transcriptomics data to better understand cellular architecture in infected tissues.
4:20 p.m. – 5:00 p.m. Reception & Networking
More information and registration on the CAIMed website: https://caimed.de/en/caimed-meet-up-ai-powered-approaches-in-infection-research/
CAIMed is funded by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the VolkswagenStiftung
