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TU Berlin

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Sven Jäger

Lupe

Research assistant

Fakultät II - Mathematik und Naturwissenschaften
Institut für Mathematik, Sekr. MA 5-2

Technische Universität Berlin
Straße des 17. Juni 136
10623 Berlin
Germany

E-Mail:
Tel.: +49 (0)30 314-21095
Fax: +49 (0)30 314-25191
Room: MA 511
Consultation hours: Tuesday 5:00 pm. Please send me an email before Tuesday noon in order to get the zoom link.

Research interests

  • Apprroximation algorithms
  • Scheduling
  • Symmetric Chain Decompositions

Teaching

Assistances
Summer 2021
Computerorientierte Mathematik II (theoretical exercises)
Winter 2020/21
Introduction to Linear and Combinatorial Optimization (ADM I)
Summer 2020
Discrete Optimization (ADM II)
Winter 2019/20
Introduction to Linear and Combinatorial Optimization (ADM I)
Summer 2019
Discrete Optimization (ADM II)
Winter 2018/19
Introduction to Linear and Combinatorial Optimization (ADM I)
Summer 2018
Computerorientierte Mathematik II (programming exercises)
Winter 2017/18
Computerorientierte Mathematik I (programming exercises)
Summer 2017
Computerorientierte Mathematik II (theoretical exercises)
Winter 2016/17
Computerorientierte Mathematik I (theoretical exercises)

Curriculum Vitae

Since July 2016
Doctoral student with Martin Skutella, Technische Universität Berlin
2013 - 2016:
Master studies in Mathematics and in Applied Computer Science at Georg-August-Universität Göttingen, Germany.
2010 - 2013:
Bachelor studies in Mathematics and in Applied Computer Science at Georg-August-Universität Göttingen, Germany.
2010:
Abitur at Hainberg-Gymnasium Göttingen

Publications

Däubel, K., Jäger, S., Mütze, T. and Scheucher, M.
On orthogonal symmetric chain decompositions.
Electronic Journal of Combinatorics, Vol. 26, pp. P3.64, 2019. Full version.

Link to original publication


Däubel, K., Jäger, S., Mütze, T. and Scheucher, M.
On orthogonal symmetric chain decompositions.
In Proceedings of the European Conference on Combinatorics, Graph Theory and Applications (EUROCOMB), pp. 611–618, 2019. extended abstract.

Link to original publication


Jäger, S. and Schöbel, A.
The blockwise coordinate descent method for integer programs.
Mathematical Methods of Operations Research, Vol. 91, pp. 357–381, 2019.

Link to publication Link to original publication



Gregor, P., Jäger, S., Mütze, T., Sawada, J. and Wille, K.
Gray codes and symmetric chains.
In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018), pp. 66:1–66:14, 2018.

Link to original publication


Jäger, S. and Skutella, M.
Generalizing the Kawaguchi-Kyan Bound to Stochastic Parallel Machine Scheduling.
In 35th Symposium on Theoretical Aspects of Computer Science (STACS 2018), pp. 43:1–43:14, 2018.

Link to original publication


Quantum coupled mutation finder: Predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming
Citation key GueltasDuezguenHerzog+2014
Author Gültas, Mehmet and Düzgün, Güncel and Herzog, Sebastian and Jäger, Sven and Meckbach, Cornelia and Wingender, Edgar and Waack, Stephan
Pages 1-17
Year 2014
ISSN 1471-2105
DOI 10.1186/1471-2105-15-96
Journal BMC Bioinformatics
Volume 15
Number 96
Month April
Publisher BioMed Central Ltd.
Abstract Background: The identification of functionally or structurally important non-conserved residue sites in protein MSAs is an important challenge for understanding the structural basis and molecular mechanism of protein functions. Despite the rich literature on compensatory mutations as well as sequence conservation analysis for the detection of those important residues, previous methods often rely on classical information-theoretic measures. However, these measures usually do not take into account dis/similarities of amino acids which are likely to be crucial for those residues. In this study, we present a new method, the Quantum Coupled Mutation Finder (QCMF) that incorporates significant dis/similar amino acid pair signals in the prediction of functionally or structurally important sites. Results: The result of this study is twofold. First, using the essential sites of two human proteins, namely epidermal growth factor receptor (EGFR) and glucokinase (GCK), we tested the QCMF-method. The QCMF includes two metrics based on quantum Jensen-Shannon divergence to measure both sequence conservation and compensatory mutations. We found that the QCMF reaches an improved performance in identifying essential sites from MSAs of both proteins with a significantly higher Matthews correlation coefficient (MCC) value in comparison to previous methods. Second, using a data set of 153 proteins, we made a pairwise comparison between QCMF and three conventional methods. This comparison study strongly suggests that QCMF complements the conventional methods for the identification of correlated mutations in MSAs. Conclusions: QCMF utilizes the notion of entanglement, which is a major resource of quantum information, to model significant dissimilar and similar amino acid pair signals in the detection of functionally or structurally important sites. Our results suggest that on the one hand QCMF significantly outperforms the previous method, which mainly focuses on dissimilar amino acid signals, to detect essential sites in proteins. On the other hand, it is complementary to the existing methods for the identification of correlated mutations. The method of QCMF is computationally intensive. To ensure a feasible computation time of the QCMF’s algorithm, we leveraged Compute Unified Device Architecture (CUDA). The QCMF server is freely accessible at http://qcmf.informatik.uni-goettingen.de/.
Link to publication Link to original publication Download Bibtex entry

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