Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


Lovric Mario, Banic Ivana, Cuder Gerald, Kern Roman, Turkalj Mirjana, Matija Rijavec, Peter Korosec

Treatment outcome clustering patterns correspond to discrete asthma phenotypes in childre

European Respiratory Journa, European Respiratory Societ, 2019

Despite widely and regularly used therapy asthma in children is not fully controlled. Recognizing the complexity of asthma phenotypes and endotypes imposed the concept of precision medicine in asthma treatment. By applying machine learning algorithms assessed with respect to their accuracy in predicting treatment outcome, we have successfully identified 4 distinct clusters in a pediatric asthma cohort with specific treatment outcome patterns according to changes in lung function (FEV1 and MEF50), airway inflammation (FENO) and disease control likely affected by discrete phenotypes at initial disease presentation, differing in the type and level of inflammation, age of onset, comorbidities, certain genetic and other physiologic traits. The smallest and the largest of the 4 clusters- 1 (N= 58) and 3 (N= 138) seemed to have a more positive pattern of treatment outcomes and were characterized by more prominent atopic markers and a predominant allelic (A allele) effect for rs37973 in the GLCCI1 gene previously associated with positive treatment outcomes in asthmatics. These patients also had a relatively later onset of disease (6+ yrs). Clusters 2 (N= 87) and 4 (n= 64) had poorer treatment success and were characterized by higher levels of airway and systemic inflammation, but varied in the type of inflammation (predominantly neutrophilic for cluster 4 and likely mixed-type for cluster 2), comorbidities (obesity for cluster 2) and platelet count (lowest for cluster 4). The results of this study emphasize the issues in asthma management due to the overgeneralized approach to the disease, not taking into account specific disease phenotypes

Cuder Gerald, Breitfuß Gert, Kern Roman

E-Mobility and Big Data - Data Utilization of Charging Operations

Proceedings of XXIX ISPIM Conference, Stockholm, 2018

Electric vehicles have enjoyed a substantial growth in recent years. One essential part to ensure their success in the future is a well-developed and easy-to-use charging infrastructure. Since charging stations generate a lot of (big) data, gaining useful information out of this data can help to push the transition to E-Mobility. In a joint research project, the Know-Center, together with the GmbH applied data analytics methods and visualization technologies on the provided data sets. One objective of the research project is, to provide a consumption forecast based on the historical consumption data. Based on this information, the operators of charging stations are able to optimize the energy supply. Additionally, the infrastructure data were analysed with regard to "predictive maintenance", aiming to optimize the availability of the charging stations. Furthermore, advanced prediction algorithms were applied to provide services to the end user regarding availability of charging stations.

Cuder Gerald, Baumgartner Christian

A data mining strategy for the search and classification of gene expression data in cancer

ÖGBMT - Jahrestagung 201, ÖGBMT - Österreichische Gesellschaft für Biomedizinische Techni, Hall in Tirol, 2018

Cancer is one of the most uprising diseases in our modern society and is defined by an uncontrolled growth of tissue. This growth is caused by mutation on the cellular level. In this thesis, a data-mining workflow was developed to find these responsible genes among thousands of irrelevant ones in three microarray datasets of different cancer types by applying machine learning methods such as classification and gene selection. In this work, four state-of-the-art selection algorithms are compared with a more sophisticated method, termed Stacked-Feature Ranking (SFR), further increasing the discriminatory ability in gene selection.
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