Computational models of brain connectivity, coupled with machine learning algorithms, make it possible to infer neuronal disease mechanisms from non-invasive functional magnetic resonance imaging (fMRI) data in humans. This illustration shows how dynamic systems models can be used for reducing complex (high-dimensional) brain activity data to a simple (low-dimensional) and mechanistically interpretable representation (Brodersen et al., PLoS Comput. Biol. 2011). Please also see the summary on ETH Life.
The Analysis of Patterns
in data poses one of the central problems in natural science and
engineering today. Large scale experiments and computer simulations
produce a huge amount of highly complex data, e.g., in proteomics of
the cell, remote sensing or acoustic and visual scene analysis.
The Machine Learning methodology extracts hidden structures from these data sources with the help of a teacher signal or often also without any supervision. For example, the mass spectra from thousands of molecular biology experiments are exploited to train complex probabilistic hidden Markov models for peptide sequencing - a data analysis approach pioneered in our group. As another example, combinations of image patches, edges and texture features are grouped by graphical models to train vision systems for image categorization and scene understanding. All these statistical models are determined by optimization algorithms which should reliably predict the hidden patterns despite experimental noise. The challenge is to develop algorithms with good generalization performance rather than greedy minimizers of empirical costs.
We currently study statistical models for data clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data. A major effort of our algorithmic and modeling work is devoted to quantify the robustness of the learned structures, i.e., to provide the data analyst with uncertainty estimates of models and methods.
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