ByMaximilian Karg

Start of the new junior research group EkoEstMed at the Albert-Ludwigs University of Freiburg!

Under the leadership of Dr. Geoffroy Andrieux, the junior research group “Entwicklungen von klinisch-orientierten Entscheidungsunterstützungen für Hochdurchsatzdaten in der personalisierten Medizin (EkoEstMed)” (engl.: Developments of clinically-oriented decision support for high-throughput data in personalised medicine) has started its work at the Albert-Ludwigs University of Freiburg on December 15th 2021.

Nowadays, precision medicine is becoming more and more of a reality and is moving further and further into everyday clinical practice. Unique characteristics of patients are identified to improve the effectiveness and accuracy of their treatments. Ambitious initiatives such as the “Medical Informatics Initiative ” (MII) actively support biomedical research in Germany to improve individualised patient care. It relies heavily on the ever-growing amount of health-related data and constantly evolving technologies. However, due to a lack of analytical methods and standardisation and harmonisation processes, only part of the existing data is used for clinical recommendations. Thus, the goal is to process and present the complex data in a comprehensible form through the development of innovative methods, such as machine learning (artificial intelligence) algorithms, and analysis processes, while at the same time making them available for exchange in an efficient and usable manner. In addition, efficient IT infrastructures as well as the development of novel visualisation standards will be created to ensure implementation and application in the clinic. This project will not only advance the processes of personalised medicine in the local clinical setting, but also MII and MIRACUM at various sites.

What are the objectives?

  1. Methods for the development, provision and use of Omics data

The aim is to develop standardisation methods to properly store, annotate and share the resulting Omics/high-throughput data, and at the same time to develop visualisation methods to process and use this high-dimensional data for clinical applications.

  1. Integration of multi-Omics data

The integration of Omics data, which on the one hand originate from different clinical laboratories and on the other hand can be used from public databases, is usually accompanied by possible batch effects. For this purpose, appropriate methods and approaches of batch effect correction will be developed and applied, and at the same time other co-factors such as gender, age and environment will be taken into account. In addition, correlation and interaction analyses of the different omics data sets will be applied and further developed, which will ultimately lead to a score for the assessment of pathogenicity.

  1. Development of tools for decision support in the clinical context

The aim is to identify molecular features that may influence treatment recommendations and disease outcome and response to treatment. Therefore, the group aims to estimate the likelihood of developing a particular type of cancer or the likelihood of recurrence and ultimately life expectancy.