In the research area Data Science, we focus on the development and application of data-driven methods for solving biological, medical, and technical problems. In close cooperation with our partners from research and industry, we develop algorithms for preprocessing and analyzing data using statistical methods and machine learning, as well as visualizing and interpreting the results. For example, within the project LeiVMed Online we develop a platform for benchmarking and visualizing data of treatments of patients in hospitals and developing prediction models for the outcome of treatments.
In many of our projects, we see the need for customized data processing pipelines due to the heterogeneity and complexity of data structures in real world processes and systems. We use applied statistics as well as numerous machine learning approaches, including black box methods (deep neural nets, random forests, gradient boosted trees, etc.) and white box methods (symbolic regression by genetic programming). We build our knowledge discovery pipelines on a variety of different frameworks, especially python scikit-learn, tensorflow, pytorch, MATLAB, and HeuristicLab.
|Stefan Anlauf, Andreas Haghofer, Karl Dirnberger, Stephan M. Winkler:
|Data-Based Prediction of Microbial Contamination in Herbs and Identification of Optimal Harvest Parameters
|International Journal of Food Engineering (IJFE), DeGruyter
|Renate Haselgrübler, Peter Lanzerstorfer, Clemens Röhrl, Flora Stübl, Jonas Schurr, Bettina Schwarzinger, Clemens Schwarzinger, Mario Brameshuber, Stefan Wieser, Stephan M. Winkler, and Julian Weghuber:
|Hypolipidemic effects of herbal extracts by reduction of adipocyte differentiation, intracellular neutral lipid content, lipolysis, fatty acid exchange and lipid droplet motility
|Stephan M. Winkler:
|Evolutionary Computation and Symbolic Regression in Scientific Modeling
|, Johannes Kepler University Linz, Computer Science, Habilitation Thesis
|Daniela Borgmann, Sandra Mayr, Helene Polin, Susanne Schaller, Viktoria Dorfer, Christian Gabriel, Stephan Winkler, and Jaroslaw Jacak:
|Single Molecule Florescence Microscopy and Machine Learning for Rhesus D Antigen Classification
|Scientific Reports 6
Transfusion of platelets is used either prophylactic to reduce the risk of clinical threatening bleedings or therapeutic to treat acute life-threatening thrombocytopathic-relevant bleedings (e.g. thrombocytopenic traumatic injuries, hematopoietic stem cell transplantation, chemotherapy). Activation and enhancement of platelet reactivity is the key step during hemostasis. After the first activation at the point of blood vessel trauma the platelets then aggregate and form a stable thrombus. This process is irreversible; the platelets are removed by macrophages during the subsequent following tissue regeneration.
In general, platelet concentrates are stored at +22°C ± 2° C in gas-permeable, sterile plastic bags; the maximum shelf life is 5 days. Anticoagulants (Heparin, Citrate) are used to prevent activation and aggregation of platelets before transfusion. Lethal sepsis in the course of platelet transfusion due to bacterial contamination can occur especially when using stored platelets at the end of their shelf life.
Aim of this project is to extend the shelf life of platelet concentrates and to characterize their actual state via real-time analysis. Utilizing high-resolution microscopy techniques on different biochemical surfaces allows monitoring activation-induced changes from the cellular - down to the molecular - level. Physico-chemical parameters during storage will be characterized. Moreover, the project is aimed to classify the interaction reactivity of (transfused) platelets following storage with the cells of the individual receiver.
FH-Prof. DI Dr. Birgit Plochberger
Daniela Borgmann MSc.
DI Dr. Jaroslaw Jacak
Sandra Mayr MSc.
Prof.(FH) DI Dr. Stephan Winkler
10/2015 - present
University of Applied Sciences, Upper Austria, Hagenberg Campus
University of Applied Sciences, Upper Austria, Linz Campus
Blutzentrale Linz, LBI Trauma Care Consult
Sponsored by Innovative Upper Austria 2020
Software technology and application