Prof. Dr. Martin Stetter

Bioinformatik und Datenbanken

Adresse: C5.210
Vöttinger Str. 27
85354 Freising
Funktionen: EDV-Beauftragter
Fakultät: Fakultät Bioingenieurwissenschaften

Sprechstunde: via Zoom nach Vereinbarung

Lehrveranstaltungen
Objektorientiertes Programmieren

Datenbanken

Angewandte Bioinformatik

Statistik

Algorithmen der Bioinformatik

Intelligente Systeme

Intelligente Datenanalyse

Ausbildung

2001

Habilitation in angewandter Informatik, TU Berlin

1994

Dr. rer. nat., Biophysik, Universität Regensburg

1990

Diplom in Biophysik, Universität Regensburg

Beruflicher Werdegang

Seit 2009

Professor für Bioinformatik und Datenbanken, Hochschule Weihenstephan-Triesdorf

seit 2002

Ernennung zum Privatdozenten, Fakultät für Informatik, Technische Universität München

2001-2008

Leiter des Kompetenzfeldes 'Bioanalogue Technologies and Solutions', Siemens AG, Corporate Technology

2000 - 2001

Senior Research Scientist, Siemens AG, Corporate Technology

1996 - 2000

Wissenschaftlicher Assistent am Institut für Informatik, Technische Universität Berlin

1992 - 1996

Leiter der EDV-Arbeitsgruppe an der Augenklinik des Universitätsklinikums Regensburg

1990 - 1992

Wissenschaftlicher Mitarbeiter am Institut für Biophysik und Physikalische Biochemie, Universität Regensburg

Publikationen

Zeitschriftenbeiträge (peer-reviewed)
- Prof. Dr. Martin Stetter, Prof. Dr. Elmar W. Lang
  
  Berechtigungen: Open Access
  
  Berechtigungen: Peer Reviewed
  
  Learning intuitive physics and one-shot imitation using state-action-prediction self-organizing maps (2021) Computational Intelligence and Neuroscience 2021 , S. 1-15. DOI: 10.1155/2021/5590445
  
  Human learning and intelligence work differently from the supervised pattern recognition approach adopted in most deep learning architectures. Humans seem to learn rich representations by exploration and imitation, build causal models of the world, and use both to flexibly solve new tasks. We suggest a simple but effective unsupervised model which develops such characteristics. The agent learns to represent the dynamical physical properties of its environment by intrinsically motivated exploration and performs inference on this representation to reach goals. For this, a set of self-organizing maps which represent state-action pairs is combined with a causal model for sequence prediction. The proposed system is evaluated in the cartpole environment. After an initial phase of playful exploration, the agent can execute kinematic simulations of the environment’s future and use those for action planning. We demonstrate its performance on a set of several related, but different one-shot imitation tasks, which the agent flexibly solves in an active inference style.
  Mehr
- Dr. rer. nat. Thorsten Schön, Prof. Dr. Martin Stetter, Ana Maria Perfeito Tomé, Carlos García Puntonet, Prof. Dr. Elmar W. Lang
  
  Berechtigungen: Peer Reviewed
  
  Physarum Learner: A bio-inspired way of learning structure from data (2014) Expert Systems with Applications 41 (11), S. 5353-5370. DOI: 10.1016/j.eswa.2014.03.002
- R. Schachtner, D. Lutter, P. Knollmüller, Ana Maria Perfeito Tomé, F. J. Theis, G. Schmitz, Prof. Dr. Martin Stetter, P. Gómez Vilda, Prof. Dr. Elmar W. Lang
  
  Berechtigungen: Open Access
  
  Berechtigungen: Peer Reviewed
  
  Knowledge-based gene expression classification via matrix factorization (2008) Bioinformatics 24 (15), S. 1688-1697. DOI: 10.1093/bioinformatics/btn245
- Rui Chang, Prof. Dr. Martin Stetter, Wilfried Brauer
  
  Berechtigungen: Peer Reviewed
  
  Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging (2008) IEEE Transactions on Knowledge and Data Engineering 20 (12), S. 1587-1600. DOI: 10.1109/TKDE.2008.89
- Markus Bundschus, Mathäus Dejori, Prof. Dr. Martin Stetter, Volker Tresp, Hans-Peter Kriegel
  
  Berechtigungen: Open Access
  
  Berechtigungen: Peer Reviewed
  
  Extraction of semantic biomedical relations from text using conditional random fields (2008) BMC Bioinformatics 9 , S. 1-14. DOI: 10.1186/1471-2105-9-207
- Rui Chang, Wilfried Brauer, Prof. Dr. Martin Stetter
  
  Berechtigungen: Peer Reviewed
  
  Modeling semantics of inconsistent qualitative knowledge for quantitative Bayesian network inference (2008) Neural Networks 21 (2-3), S. 182-192. DOI: 10.1016/j.neunet.2007.12.042
Beiträge in Monografien, Sammelwerken, Schriftenreihen
- Dr. rer. nat. Thorsten Schön, Prof. Dr. Martin Stetter, O. Belova, A. Koch, Ana Maria Perfeito Tomé, Prof. Dr. Elmar W. Lang
  
  Physarum Learner: A Slime Mold Inspired Structural Learning Approach (2016) A. Adamatzky (Hrsg.), Advances in Physarum Machines. Springer International , S. 489-519.
- Andreas Nägele, Mathäus Dejori, Prof. Dr. Martin Stetter
  
  Robust Learning of High-dimensional Biological Networks with Bayesian Networks (2008) Schuster A. (eds) Robust Intelligent Systems. Springer, London , S. 139-161. DOI: 10.1007/978-1-84800-261-6_7
- Prof. Dr. Martin Stetter, Andreas Nägele, Mathäus Dejori
  
  GeneSim™: Intelligent IT Platform for the Biomedical World (2007) Schuster A.J. (eds) Intelligent Computing Everywhere. Springer, London , S. 171-194. DOI: 10.1007/978-1-84628-943-9_9
- Gustavo Deco, Prof. Dr. Martin Stetter, Miruna Szabo
  
  Biased Competition and Cooperation: A Mechanism of Mammalian Visual Recognition? (2007) Osaka N., Rentschler I., Biederman I. (eds) Object Recognition, Attention, and Action. Springer, Tokyo , S. 187-203. DOI: 10.1007/978-4-431-73019-4_13
Beiträge zu wissenschaftlicher Konferenz/Tagung
- Dr. rer. nat. Thorsten Schön, Prof. Dr. Martin Stetter, Ana Maria Perfeito Tomé, Prof. Dr. Elmar W. Lang
  
  Berechtigungen: Peer Reviewed
  
  A new Physarum Learner for Network Structure Learning from Biomedical Data (2013) 6th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2013), 11.-14. Februar 2013, Barcelona .
- Dr. rer. nat. Thorsten Schön, Prof. Dr. Martin Stetter, Prof. Dr. Elmar W. Lang
  
  Berechtigungen: Peer Reviewed
  
  Structure Learning for Bayesian Networks using the Physarum Solver (2012) 11th International Conference on Machine Learning and Applications (ICMLA 2012), 12.-15. Dezember 2012, Boca Raton . DOI: 10.1109/ICMLA.2012.89
- Rui Chang, Prof. Dr. Martin Stetter, Wilfried Brauer
  
  Berechtigungen: Open Access
  
  Berechtigungen: Peer Reviewed
  
  Hierarchical Inconsistent Qualitative Knowledge Integration for Quantitative Bayesian Inference (2007) 2nd International Conference on Intelligent Systems and Knowledge Engineering (ISKE2007), ChengDu . DOI: 10.2991/iske.2007.55
- Andreas Nägele, Mathäus Dejori, Rui Chang, Prof. Dr. Martin Stetter
  
  Berechtigungen: Peer Reviewed
  
  Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures (2007) The 18th European Conference on Machine Learning (ECML) and the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Warschau, 17. - 21. September 2007 , S. 238-249. DOI: 10.1007/978-3-540-74958-5_24
- Anton Schwaighofer, Mathäus Dejori, Volker Tresp, Prof. Dr. Martin Stetter
  
  Berechtigungen: Peer Reviewed
  
  Structure Learning with Nonparametric Decomposable Models (2007) Artificial Neural Networks – ICANN 2007. ICANN 2007, 09.-13. September 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg , S. 119-128. DOI: 10.1007/978-3-540-74690-4_13
- Rui Chang, Prof. Dr. Martin Stetter
  
  Berechtigungen: Peer Reviewed
  
  Quantitative Bayesian Inference by Qualitative Knowledge Modeling (2007) International Joint Conference on Neural Networks, 12.-17. August 2007, Orlando , S. 2563-2568. DOI: 10.1109/IJCNN.2007.4371362
Patente
- Prof. Dr. Martin Stetter, Linda Tambosi, Gustavo Deco
  
  Verfahren zur rechnergestützten Verarbeitung von in einem Sensornetzwerk erfassten Messwerten (2008) German patent 10 2007 014 650 .
- Prof. Dr. Martin Stetter, Rui Chang
  
  Method to obtain quantitative predictions based on the fusion of qualitative statements (2007) German patent 10 2006 033 267 .
- Prof. Dr. Martin Stetter, Michael Hofstetter, Bernd Schürmann
  
  Recheneinheit für logische Operationen im Nanometer-Masstab auf der Basis von Biomolekülen (2007) German Patent 10 2006 021 761 WIPO patent WPCT/EP2007/054385 .
Zeitschriftenbeiträge
- Andreas Nägele, Mathäus Dejori, Prof. Dr. Martin Stetter
  
  Robust learning of Bayesian networks in high dimensions (2007) AISB Journal .

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Zurück zur Übersicht

Prof. Dr. Martin Stetter

Lehrveranstaltungen

Ausbildung

Beruflicher Werdegang

Publikationen

Zeitschriftenbeiträge (peer-reviewed)

Learning intuitive physics and one-shot imitation using state-action-prediction self-organizing maps (2021) Computational Intelligence and Neuroscience 2021 , S. 1-15. DOI: 10.1155/2021/5590445

Physarum Learner: A bio-inspired way of learning structure from data (2014) Expert Systems with Applications 41 (11), S. 5353-5370. DOI: 10.1016/j.eswa.2014.03.002

Knowledge-based gene expression classification via matrix factorization (2008) Bioinformatics 24 (15), S. 1688-1697. DOI: 10.1093/bioinformatics/btn245

Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging (2008) IEEE Transactions on Knowledge and Data Engineering 20 (12), S. 1587-1600. DOI: 10.1109/TKDE.2008.89

Extraction of semantic biomedical relations from text using conditional random fields (2008) BMC Bioinformatics 9 , S. 1-14. DOI: 10.1186/1471-2105-9-207

Modeling semantics of inconsistent qualitative knowledge for quantitative Bayesian network inference (2008) Neural Networks 21 (2-3), S. 182-192. DOI: 10.1016/j.neunet.2007.12.042

Beiträge in Monografien, Sammelwerken, Schriftenreihen

Physarum Learner: A Slime Mold Inspired Structural Learning Approach (2016) A. Adamatzky (Hrsg.), Advances in Physarum Machines. Springer International , S. 489-519.

Robust Learning of High-dimensional Biological Networks with Bayesian Networks (2008) Schuster A. (eds) Robust Intelligent Systems. Springer, London , S. 139-161. DOI: 10.1007/978-1-84800-261-6_7

GeneSim™: Intelligent IT Platform for the Biomedical World (2007) Schuster A.J. (eds) Intelligent Computing Everywhere. Springer, London , S. 171-194. DOI: 10.1007/978-1-84628-943-9_9

Biased Competition and Cooperation: A Mechanism of Mammalian Visual Recognition? (2007) Osaka N., Rentschler I., Biederman I. (eds) Object Recognition, Attention, and Action. Springer, Tokyo , S. 187-203. DOI: 10.1007/978-4-431-73019-4_13

Beiträge zu wissenschaftlicher Konferenz/Tagung

A new Physarum Learner for Network Structure Learning from Biomedical Data (2013) 6th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2013), 11.-14. Februar 2013, Barcelona .

Structure Learning for Bayesian Networks using the Physarum Solver (2012) 11th International Conference on Machine Learning and Applications (ICMLA 2012), 12.-15. Dezember 2012, Boca Raton . DOI: 10.1109/ICMLA.2012.89

Hierarchical Inconsistent Qualitative Knowledge Integration for Quantitative Bayesian Inference (2007) 2nd International Conference on Intelligent Systems and Knowledge Engineering (ISKE2007), ChengDu . DOI: 10.2991/iske.2007.55

Structure Learning with Nonparametric Decomposable Models (2007) Artificial Neural Networks – ICANN 2007. ICANN 2007, 09.-13. September 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg , S. 119-128. DOI: 10.1007/978-3-540-74690-4_13

Quantitative Bayesian Inference by Qualitative Knowledge Modeling (2007) International Joint Conference on Neural Networks, 12.-17. August 2007, Orlando , S. 2563-2568. DOI: 10.1109/IJCNN.2007.4371362

Patente

Verfahren zur rechnergestützten Verarbeitung von in einem Sensornetzwerk erfassten Messwerten (2008) German patent 10 2007 014 650 .

Method to obtain quantitative predictions based on the fusion of qualitative statements (2007) German patent 10 2006 033 267 .

Recheneinheit für logische Operationen im Nanometer-Masstab auf der Basis von Biomolekülen (2007) German Patent 10 2006 021 761 WIPO patent WPCT/EP2007/054385 .

Zeitschriftenbeiträge

Robust learning of Bayesian networks in high dimensions (2007) AISB Journal .