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Statistical Approaches for biological networks
Seminar
Focus of the seminar are statistical inference approaches for
biological networks from high-throughput data. The seminar is open to everybody. I especially invite students interested in systems
biology. Moreover, the seminar is part of the Graduate School
program in the Cluster of Excellence Simulation Technology.
Content of the seminar
- Stochastic modeling approaches for biological (espcially gene regulatory) networks, in particular, Bayesian and dynamic Bayesian networks
- Statistical learning approaches (Maximum likelihood and Bayesian methods), with various applications to network inference in biology
- Sampling methods for analyzing probability distributions
I will give an introductory course about stochastic modeling
approaches in the beginning of the seminar (2-3 weeks), in which the
basics for the following talks are provided. Then every participant
presents a book chapter or a research paper that falls within the
scope of the seminar. A list of suggested papers is available
here.
Organisational information
| Time | Thursday, 14:00 o'clock |
| Place | Pfaffenwaldring 9, seminar room 3.243 |
Meetings
| 07.05. | Bayesian networks, dynamic Bayesian networks, and Hidden Markov models (NR): A comparison of likelihoods for dynamic stochastic models of biological networks |
| 14.05. | Maximum likelihood and Bayesian estimation (NR) |
| 28.05. | Bayesian estimation for single parameter models (Tafelvortrag, Christian Breindl) |
| 18.06. | Introduction to multiparameter models (Andreas Benzing, Martin Falk) Vortragsfolien |
| 02.07. | Posterior simulation (Tafelvortrag, Andrei Kramer) |
| 09.07. | Bayesian regression approach to the inference of regulatory networks from gene expression data (Tafelvortrag, Adem Gürses) |
| 16.07. | Markov chain Monte Carlo without likelihoods (Julian Heinrich) |
| 23.07. | A Bayesian approach to reconstructing genetic regulatory networks with hidden factors (Benjamin Heinrich) |
For further information, contact Nicole Radde.
Universität Stuttgart
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