Home // Research // Applied AI and Big Data // Life Science and Medicine // Projects

Leading Principal Investigator

Prof. Dr. Jens Meiler

Institute for Drug Discovery

Leipzig University

jens.meiler@medizin.uni-leipzig.de

Team Leads

Dr. Iryna Okhrin

Center for Interdisciplinary Digital Sciences (CIDS)

TUD Dresden University of Technology

iryna.okhrin@tu-dresden.de

Dr. Christian Martin

Department of computer science, Data Base Group

Leipzig University

cmartin@informatik.uni-leipzig.de

Dr. Jan Ewald

Leipzig University

jan.ewald@uni-leipzig.de

Projects

In Life Science & Medicine, we are involved in the following research areas:

Drug Design
Biomedical Image Analysis
Radiomics
Bioinformatics
Health Care
Veterinary Medicine
Method Development

Drug Design

AI driven design of CAR T-cell therapies

Computational B-cell epitope prediction using artificial intelligence methods

Computational peptide design to target therapy-resistant tumors using physics- and machine learning-based methods

Computational protein design for novel immunotherapeutic drugs using artificial intelligence

Drug Discovery on the SpiNNaker2 Neuromorphic Supercomputer

Experimental and Computational High-Throughput Characterization of Protein-Protein Interactions

Next Generation Computer-aided Drug Discovery for Small Molecules

Predicting Protein Conformational Ensembles Using AlphaFoldTemplate

The structural basis of functional selectivity in the neuropeptide Y receptor Y2R

Biomedical Image Analysis

Content-Adaptive Biomedical Image Understanding

DeepLineage

GloBIAS

GPU-Accelerated Image Analysis using CLIJ and clEsperanto

NFDI4BioImage

Radiomics

Decision support in acute stroke

Bioinformatics

Biomarker Prediction from Genomic Sequencing Data

GRAMMY

MIRACLE

Machine Learning-based Gene Annotation in a Genome-wide Context

Mapping Single-Cell Data between Species by Combined Deep Learning and Explainable AI

SaxoCell

SaxoCell – 2nd Phase

Health Care

Intelligent Patient Record (iPA)

Standardization of Clinical Data for AI Applications

Synthetic Tabular Data Generation in the Medical Domain

Veterinary Medicine

Analysis of Equinal Szintigraphy Images

Prediction of structural epilepsy in dogs

Method Development

Scaling transformer neural networks to gigapixel images

Learning Spatiotemporal Models from Limited Noisy Data

funded by:

Gefördert vom Bundesministerium für Bildung und Forschung.

Gefördert vom Freistaat Sachsen.

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