Biomedical subjects

Bernd Freisleben

Publications and source records attributed to Bernd Freisleben.

2 recordsLinked to original sources

PubMed2004

Investigating the dynamic behavior of biochemical networks using model families.

MOTIVATION: Supporting the evolutionary modeling process of dynamic biochemical networks based on sampled in vivo data requires more than just simulation. In the course of the modeling process, the modeler is typically concerned not only with a single model but also with sequences, alternatives and structural variants of models. Powerful automatic methods are then required to assist the modeler in the organization and the evaluation of alternative models. Moreover, the structure and peculiarities of the data require dedicated tool support. SUMMARY: To support all stages of an evolutionary modeling process, a new general formalism for the combinatorial specification of large model families is introduced. It allows for automatic navigation in the space of models and excludes biologically meaningless models on the basis of elementary flux mode analysis. An incremental usage of the measured data is supported by using splined data instead of state variables. With MMT2, a versatile tool has been developed as a computational engine intended to be built into a tool chain. Using automatic code generation, automatic differentiation for sensitivity analysis and grid computing technology, a high performance computing environment is achieved. MMT2 supplies XML model specification and several software interfaces. The performance of MMT2 is illustrated by several examples from ongoing research projects. AVAILABILITY: http://www.simtec.mb.uni-siegen.de/ CONTACT: wiechert@simtec.mb.uni-siegen.de.

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PubMed2003

Analyzing EEG signals using the probability estimating guarded neural classifier.

This paper introduces a neural network architecture for classifying feature vectors symbolizing portions (or segments) of an electroencephalogram (EEG) trace of a human subject. This classification task is the one that is typically required when developing a so-called brain-computer interface (BCI), which analyzes the EEG signals of a subject in order to "understand" the subject's thoughts. However, instead of merely saying which "category of thoughts" (i.e., which class) the respective input feature vector belongs to, the network described here estimates the probabilities of an EEG segment being associated with each individual class. The network, which is called PeGNC (for probability estimating guarded neural classifier), is tested with two kinds of experiments. In the first experiment, the alpha-rhythm associated with a human subject closing the eyes is detected online with the help of a frequency-based representation. Since the EEG signal is, in general, always a mixture of numerous action potentials generated simultaneously and it is, thus, very likely that mental activities result in overlapping classes, it is reasonable to believe that the PeGNC network--which does not select any one single class, but determines probability values for each mental category--is particularly suitable for this kind of EEG analysis. The second experiment deals with this issue on the basis of an offline analysis of simulated data.

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