Dresden 2020 – wissenschaftliches Programm
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DY 12.2: Vortrag
Montag, 16. März 2020, 15:15–15:30, SCH A251
Towards a grammar of probabilistic models for large biological networks — •Philipp Fleig1 and Ilya M. Nemenman2 — 1University of Pennsylvania, Philadelphia, USA — 2Emory University, Atlanta, USA
Biological interaction networks such as biological neural networks, amino acid sequences in proteins, etc. are critical to the functioning of any living system. The trend of modern experiments is to record data with a rapidly increasing number of simultaneously measured network variables. Inferring models for such complex data is becoming increasingly more difficult, since one is confronted with a combinatorial explosion in the number of possible interactions between variables. Here we present first steps of an approach to overcome this obstacle. We investigate the question whether a small set of carefully chosen statistical models suffices to describe rich phenomenology in data of biological networks. As candidate models for this grammar we consider low-rank approximation, clustering, sparsity, etc.. We discuss the distribution of eigenvalues and pairwise correlations characteristic for each model, working under the assumption that they serve as key indicators for the phenomenology described by a model. We provide examples of modelling data of Ising spin systems and outline a vision for how combinations of models in the grammar cover a large part of model space occupied by biological networks.