Biomedical Data Science

Biomedical Data Science deals with management, integration and analysis of large and complex data sets from life and medical sciences. By applying modern and computer-based methods/tools, the complex information and analysis results of those large data sets are made understandable and usable. The overall objective is to push the boundaries of our knowledge in life sciences and healthcare. Ultimately, the knowledge gained should lead to new, verifiable hypotheses and support diagnostics and decision-making in the clinical context. Key areas in biomedical data science include data management, statistics, visualisation and machine learning, in particular, deep learning methods.  

Research

  • Data Management and Data Integration
  • Decision making
  • Visual Analytics
  • Artificial Intelligence
  • Landscape Genomics
  • Ecological Genomics

Biomolecular Structure and Function

Biomolecules are chemical molecules that form organisms and give function to these. There are different classes of biomolecules with each their specific properties and tasks. For example, some biomolecules can store information (DNA), others form, disassemble or change other molecules (proteins) and some biomolecules serve as energy source for organisms (carbohydrates). Most times, biomolecules are highly complex three-dimensional objects; their structure significantly influences their function. Biomolecules have a lot of different interesting properties: In principle, they all can interact with each other, some are part of an organism's immune system, others may be used as target for pharmaceutical drugs or may in fact be used as pharmaceutical drugs themselves.  

Scientists at IBMI work on investigating the structure and function of biomolecules. Of special interest are proteins, naturally occurring chemical molecules as well as synthetic molecules that potentially could be used as drugs. Main research areas are the development of computer-based methods to predict a biomolecule´s three-dimensional structure, their function and mutual interactions but also the evolution and origin of biomolecules.  

Research

  • Structure Prediction
  • Protein Evolution
  • Structure Visualization
  • Computational Chemistry
  • Biophysics
  • Computer-Aided Drug Design
Publications


Principles and Applications of CF2X Moieties as Unconventional Halogen Bond Donors in Medicinal Chemistry, Chemical Biology, and Drug Discovery
J. Med. Chem. 66(15), 2023, https://doi.org/10.1021/acs.jmedchem.3c00634
Contributions by: Böckler Group

State of the Art of Molecular Visualization in Immersive Virtual Environments
Computer Graphics Forum, 42, 2023, https://doi.org/10.1111/cgf.14738
Contributions by: Krone Group

Exploring protein-protein interactions at the proteome level
Structure, 30(4), 2022, https://doi.org/10.1016/j.str.2022.02.004
Contributions by: Kohlbacher and Lupas Group

Computer-based Omics

The add-on omics refers to research areas within life sciences that investigate a molecular level in cells in its entirety.  Currently, we distinguish five such molecular levels: the genome (Genomics, DNA), epigenome (Epigenetics, change of DNA), transcriptome (Transcriptomics, RNA), proteome (Proteomics, proteins) and metabolome (Metabolomics, further chemical molecules in cells). Analysing a single molecular level is not only demanding on a technical level but it is a challenge for data management and analysis due to the sheer amount of information stored on a single molecular level (the human genome, e.g., consists of ~3.2 billion nucleotides sc. letters). It needs specialised computer-based approaches developed in bio- and medical informatics to use these data sets and to support their biological interpretation for different species. The complexity increases further if molecular levels across different individuals are being examined (meta-omics) and/or if several levels are being analysed together in an integrative approach (multi-omics).

Scientist at IBMI develop methods and algorithms for the analysis and interpretation of omics-data; those aim to better understand the biology of single cells, tissues, organisms and even entire ecosystems in a healthy as well as in a diseased state. The scientists work focuses on, amongst others, to improve diagnostics and treatment of diseases, to identify new therapeutic substances within microorganism, to examine plants´ adaptation to their environment and to enable the analysis of historical organic specimens.  

People

Franz Baumdicker
Manfred Claassen
Daniel Huson
Oliver Kohlbacher
Sven Nahnsen
Kay Nieselt
Stephan Ossowski
Nico Pfeifer
Detlef Weigel
Nadine Ziemert

Research

  • Genomics and Metagenomics
  • Epigenetics
  • Transriptomics and Metatransriptomics
  • Proteomics
  • Metabolomics
  • Single-cell analysis
  • Multiomics
  • GWAS
  • Comparative Genomics
Publications


FunARTS, the Fungal bioActive compound Resistant Target Seeker, an exploration engine for target-directed genome mining in fungi
Nucleic Acids Research, 51(1), 2023, https://doi.org/10.1093/nar/gkad386
Contributions by: Ziemert Group

MeganServer: facilitating interactive access to metagenomic data on a server
Bioinformatics, 39(3), 2023, https://doi.org/10.1093/bioinformatics/btad105
Contributions by: Huson Group

OmicsTIDE: interactive exploration of trends in multi-omics data
Bioinformatics Advances, 3(1), 2023, https://doi.org/10.1093/bioadv/vbac093
Contributions by: Krone and Nieselt Group

Simulation-based inference of differentiation trajectories from RNA velocity fields
Cell Reports Methods 2(12), 2022, https://doi.org/10.1016/j.crmeth.2022.100359
Contributions by: Claassen Group

Infection and Immunology

Infectious diseases are caused by pathogenic microorganisms and viruses and pose a potential threat in our globalised world. To be able to detect pathogens, to understand their disease mechanisms and development and to identify new therapeutic agents may help to minimize their risks. Research on infections is closely linked to research on the immune system. The immune system is not only a natural system which fights infections but also an important player in various diseases not caused by pathogens, such as cancer. A deeper understanding of the immune system helps to develop new methods to treat these diseases.  

Scientists at IBMI develop methods and algorithms to study the mechanisms and development of pathogens and to identify new therapeutic agents. They also develop methods to improve our understanding of the immune system and to predict its behaviour towards pathogens or other disease states to specifically utilise its abilities to combat diseases.  

People

Manfred Claassen
Oliver Kohlbacher
Sven Nahnsen
Kay Nieselt
Stephan Ossowski
Nico Pfeifer
Nadine Ziemert

Research

  • Pathogen detection
  • Host-pathogen interaction
  • Antibiotic natural product discovery
  • Antibiotic resistance
  • Immunoinformatics
  • Pathogen evolution analysis
  • Treatment decision support
Publications


PlasmoFAB: a benchmark to foster machine learning for Plasmodium falciparum protein antigen candidate prediction
Bioinformatics, 39(1), 2023, https://doi.org/10.1093/bioinformatics/btad206
Contributions by: Pfeifer Group

Influence of Staphylococcus aureus Strain Background on Sa3int Phage Life Cycle Switches
Viruses, 14, 2022, https://doi.org/10.3390/v14112471
Contributions by: Nieselt Group

Medical Informatics

Medical informatics is a core area on the way to a digitalised medicine in which ideally all medical information is being stored, systematically processed and finally made accessible to scientists and practitioners.  The overall aim is to provide a central key piece for the best possible preventive care and optimised patient care. The main challenges in medical informatics are the huge amount of collected and heterogeneous data and its security. Data of human origin needs to be optimally protected from unauthorised access while always guaranteeing the individual full control over their own data.  

Scientists at IBMI work on setting up the necessary IT infrastructures. They also develop modern algorithms for collecting, processing, securing and making this data available. Another focus is the development of new methods and algorithms to gain knowledge from such data and its interpretation using AI, amongst others.  

People

Mete Akgün
Carsten Eickhoff
Oliver Kohlbacher
Thomas Küstner
Nico Pfeifer

Research

  • Clinical Information Systems
  • Medical Data Management
  • Medical Data Privacy
  • Privacy Preserving Machine Learning
  • eHealth and mHealth
  • Medical Image Analysis
  • Treatment decision support
Publications


Efficient privacy-preserving whole-genome variant queries
Bioinformatics, 38 (8), 2022, https://doi.org/10.1093/bioinformatics/btac070
Contributions by: Akgün and Kohlbacher Group

Sparse Activations for Interpretable Disease Grading
medRxiv (2023), https://doi.org/10.1101/2023.03.07.23286895
Contributions by: Baumgartner and Berens Group

Translational Bioinformatics

Translational research is often described as from bench to bedside. Essentially, this refers to the transformation of knowledge and findings from basic research (bench) into products, devices and applications used in diagnostics and treatment of diseases (bedside). Enabling and promoting such transformations helps to increase human health. Within this multidisciplinary field, translational bioinformatics lies at the interface of bioinformatics and clinical research. Its aim is to develop and provide tools which integrates and connects the massively growing mass of molecular and clinical data to expand possibilities in diagnostics and therapeutic decisions.

Scientists at IBMI combine bioinformatic research with medical expertise from research and practice. Their transformative approaches lead to directly usable databases, tools and applications. Those enable, e.g., the discovery of new antibiotics, the identification of molecular cause in complex diseases or the prediction of life-threatening conditions, even before these occur.  

People

Mete Akgün
Manfred Claassen
Carsten Eickhoff
Oliver Kohlbacher
Sven Nahnsen
Stephan Ossowski
Nico Pfeifer
Nadine Ziemert

Research

  • Clinical Diagnostics
  • Clinical Bioinformatics
  • Personalized Medicine
  • Diagnostic decision support
  • Antibiotic discovery
  • Genome privacy
  • Treatment decision support
Publications


Resurrecting ancestral antibiotics: unveiling the origins of modern lipid II targeting glycopeptides
Nat. Commun.,14, 7842, 2023, https://doi.org/10.1038/s41467-023-43451-4
Contributions by: Ziemert Group

Predicting functional effects of ion channel variants using new phenotypic machine learning methods
PLoS Comp. Biol., 9(3), 2023, https://doi.org/10.1371/journal.pcbi.1010959
Contributions by: Pfeifer Group

Neonatal apnea and hypopnea prediction in infants with Robin sequence with neural additive models for time series
medRxiv, 2023, https://doi.org/10.1101/2023.03.14.23287021
Contributions by: Kohlbacher Group

 

Privacy settings

Our website uses cookies. Some of them are mandatory, while others allow us to improve your user experience on our website. The settings you have made can be edited at any time.

or

Essential

in2code

Videos

in2code
YouTube
Google